Model Lifecycle

Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI Researcher

Sep 8, 2026 · 1:28:12

Harshul Jain of Audible and independent AI researcher Tanmay Sah teach a two-hour workshop building LLM inference optimization from first principles on Mistral 7B. They show KV cache costs 131 KB per token, so 16,000 tokens across 80 concurrent users needs 42 GB of GPU memory, driving the pain points of memory, TTFT, and throughput. Sah covers quantization, multi-head, multi-query, grouped-query, and latent attention, plus flash attention, using his 'ostrich' and 'world cup' teaching algorithms. Jain covers paged attention, continuous batching, prefix caching, and KV quantization, benchmarked against a Hugging Face baseline on vLLM. Their benchmarking found vLLM and SGLang statistically tied on standard workloads, but SGLang three to four times faster once agentic branching enters.

Can LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AI

Aug 27, 2026 · 30:00

Simran Arora, principal scientist at Together AI, argues multi-GPU communication, not single-GPU compute, is AI's bottleneck, and introduces ParallelKittens and ParallelKernelBench to test whether LLMs can write fast multi-GPU kernels. From A100 to B200, BF16 tensor core throughput improved 7.2x vs 3x intra-node communication, leaving PyTorch+NCCL baselines below 50% of communication-aware roofline on most problems. ParallelKittens adds a dozen lines to a single-GPU kernel and runs in production at Together and Cursor; in ParallelKernelBench's 87 problems, the best model solves 28 zero-shot (22 faster), and more samples lift correctness to 36 but fast-and-correct stalls near 31%. Models compile after retries but stall on ordering and transfer choices; a bash-based agent hits 35 correct.

KV Cache-Aware Routing and P/D Disaggregation on Kubernetes — Yuchen Fama & Ashish Kamra, Red Hat

Aug 27, 2026 · 21:48

Yuchen Fama and Ashish Kamra of Red Hat argue that public inference benchmarks hide the chaotic reality of agentic workloads, and show how KV cache-aware routing plus prefill/decode disaggregation in the open source llm-d framework tackles it. Red Hat's traces show agentic sessions running from a few turns to 3,000, cache hit rates over 90%, and input-output token ratios past 100:1, making a 10x cost gap between cached and uncached tokens. A live demo shows routing reusing cache on the same pod cutting time from 3 seconds to 1, while a fresh system prompt pays the full 3 again. For prefill/decode disaggregation, they report P99 inter-token latency dropping from roughly 900 milliseconds to about 100 across 16 H100s serving gpt-oss, but note it only wins in the middle concurrency band and requires RDMA or RoCE to move KV caches. Their closing case study runs GLM 5.2 on H200s with three prefill workers to one decode, achieving 4x faster time to first token and 60% more requests.

GTM Engineering: The Technical Bits — Everett Berry, Clay

Aug 26, 2026 · 19:04

Everett Berry of Clay explains GTM engineering, the discipline of letting go-to-market teams ship as fast as engineering teams, by covering its hardest technical areas. Data first: accounts never hold still, so teams waterfall across hundreds of vendors—Forager alone yields half of phone numbers—and must run evals to trust providers. Orchestration is the second problem, a data engineering puzzle across 20–30 tools that sync behind your back; Clay's fix is a graph of general purpose nodes. Third are long-running agents, one per account, dormant until triggered, holding state across a deal cycle; human-agent interface is the hardest problem. Execution is the fourth: with cold email reply rates at 0.5–1%, reps spread domains, route replies home, and suppress channels when meetings book.

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

Aug 22, 2026 · 15:54

DigitalOcean's Archana Kamath and Tyler Gillam argue that no single best AI model exists, so choosing models by benchmark leaderboards is the wrong instinct; the right model depends on the request's task, cost, latency, system prompts, and end-user preferences. They demo their open-source inference router, built into DigitalOcean's AI-native cloud, which uses a purpose-built mixture-of-experts model to decide in under 200 milliseconds, costs nothing extra, and needs no code changes. In a live coding-agent comparison, the router matched premium quality while spending 14 cents versus 44 cents over a session and scored 90% correctness versus Opus's 95% with fewer tokens and faster speed. They frame routing as a foundation layer for evals, caching, and personalization, with no vendor lock-in.

Your Fine-Tuned Model Is Tech Debt: A 50x ROI House of Cards — Dan Bjornn, Lease End

Aug 20, 2026 · 16:39

Dan Bjornn, senior data scientist at Lease End, explains why his team's fine-tuned LLM for customer intent—despite bringing in $12 million at a 50x ROI—became tech debt. The fine-tuning pipeline took a week per retrain, with training itself the shortest step, and each fix caused regressions, so bugs were triaged by tolerable customer pain. He calls this the calcification tax: the model locked them into one provider and an outdated architecture, preventing upgrades. The rebuild swapped the tuned model for skills, prompts, and context on a model-agnostic framework, letting fixes ship in under an hour as uploaded files. Accuracy went up, cost per message rose, but total cost fell. Bjornn concludes fine-tune only when you cannot call a frontier model, and even then the decision must beat the tax.

Don’t be data poor — Anuj Iravane, Anterior

Aug 19, 2026 · 16:46

Anuj Iravane, who leads AI at Anterior, explains why the company builds synthetic medical data by reversing its inference workflow: the real faxed records it needs most are protected patient data its contracts forbid keeping. With about 70% of medical communication still moving by fax, Anterior starts from a label, samples a reasoning trace from policies modeled explicitly as decision trees, then builds a coarse-to-fine patient journey and documents, making labels correct by construction. Clinicians own the pipeline as interchangeable skills, and roughly 90% of datasets are now synthetic; in a blind review they separated synthetic from real only about 60% of the time. The payoff is creating edge cases on demand and testing workflows before deployment instead of waiting on customer data.

200 Million Patient Interactions Later — Vivek Muppalla, Hippocratic AI

Aug 19, 2026 · 20:40

Vivek Muppalla, head of AI engineering at Hippocratic AI, argues clinically safe AI voice agents can end healthcare rationing, citing 200 million conversations and 60-plus health systems. He details Polaris, their architecture running 31 models per call—one central model plus 30 specialists for labs, medications, scheduling—in parallel after each checks if it needs to speak. Their decoder-only audio LLM adds context and domain knowledge for drug recognition; single-word answers get rescored because 'no' heard as 'now' is catastrophic. With 4-bit quantization, speculative decoding, and KV cache compression, latency savings are reinvested into intelligence. But at 10,000 calls a day, 99% accuracy means 100 wrong appointments, so they use 7,000 clinicians and 450 tests to catch a 1% failure rate.

From Ambient Documentation to Clinical Intelligence — Chaitanya Asawa, Abridge

Aug 19, 2026 · 21:35

Chaitanya Asawa of Abridge argues that everything in healthcare sits downstream of the doctor-patient conversation, and that the administrative machinery around it can be automated. Abridge began with clinical documentation to end 'pajama time' and reached 300 of the largest US health systems in two to three years. For its contextual clinical decision support, quality is paramount because the generator-verifier gap is tiny; Abridge has physicians write independent rubrics, adjudicated into a final rubric, with expert-calibrated LLM judges scoring responses. At a run rate of 100 million medical conversations a year, Abridge decomposes each note into sections and post-trains smaller models per section, betting its unique dataset and narrow problems can outrun frontier models.

Generative Video at the Speed of Light — Keegan McCallum, uRun

Aug 18, 2026 · 8:43

Keegan McCallum, founder of inference provider uRun, argues that generative video's interesting axis is no longer quality but efficiency and long-horizon generation. He shows Helios, a distillation of Wan 2.1 14b, generating clips in real time for ~one-hundredth the cost of a slower frontier-quality clip, and notes at least 40 real-time/long-horizon models released this year. Ten dollars buys three hours of continuous generative video, and fifty buys fifteen; this unlocks magic-mirror webcam transformations, visual mediums for people who don't think in text, and content creation where you steer a generation in under a second. The hard part is serving: global GPUs, WebRTC with ICE/TURN, and synchronized streaming pipelines; uRun is building a React component, Python runtime, and MCP/CLI.

Voice agents with Realtime Video — Sidney Primas, LemonSlice

Aug 18, 2026 · 26:36

LemonSlice CTO Sidney Primas explains how his startup builds real-time video avatars by pointing world models at humans. A Microsoft partnership put a Teddy Roosevelt avatar in a replica Oval Office, generating continuously for eight hours with no reset. He argues audio embeddings drive emotion, and because avatars only look backward, errors compound, so LemonSlice trains with an attention mask and collapses roughly 30 denoising steps to one. He says serving video costs about the same as a voice model, and that the model harness, which orchestrates GPU/CPU threads and queues to avoid stutters, holds much durable value. He predicts an emotion engine for better reactions and an end-to-end EQ layer within two or three years, taking user video and audio and outputting avatar video and audio.

Training Krea 2: What matters in generative model training — Sangwu Lee, Krea.ai

Aug 18, 2026 · 21:46

Sangwu Lee of Krea.ai explains what went into training Krea 2, its open-sourced image foundation model, arguing that once architecture is locked, data is everything. To keep stylistic diversity over the 'most boring average person' consistency of production models, Krea filters billions of images: no AI images, OCR and vision-language captions, hash and embedding dedup, distilled VLM classifiers, sparse autoencoders as unsupervised taggers for watermarks and borders. World knowledge is checked against Wikipedia concepts by PageRank. Training goes from 256 to 1K resolution through pre-training, mid-training, SFT, preference optimization, and GRPO-style RL, plus a prompt expander. Lee wants a single clean transformer without VAEs/text encoders and VLM-generated bounding boxes or scene graphs.

The Rise of CaaS: Context-as-a-Service for Agentic AI — Omer Primor, Bright Data

Aug 14, 2026 · 22:20

Omer Primor of Bright Data argues that rented AI search and context-as-a-service (CaaS) lose to a self-built pipeline once query volume passes a tipping point. The web is context, not data, and it decays: social content goes stale within a day, news/finance/retail within 30 days, so context is never a one-time snapshot. His test enriching 100 sponsor companies across 25 fields found general search beat dedicated CaaS vendors on coverage, because CaaS only answers from data it already holds; costs were similar, but frequency is the real killer — every repeated query costs the same when nothing changed. He built scrapers for LinkedIn, jobs, and Crunchbase in a day, priced setup at $5,000, and put the crossover just over 15,000 entities. Owned context compounds while rented decays.

Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain

Aug 12, 2026 · 20:02

Vivek Trivedy, lead of applied research at LangChain, argues that improving agents is fundamentally a data mining problem: ship agents, collect traces, then mine them to drive continual learning. He claims observability and continual learning are the same problem because agents operating in environments produce trace data, which is the substrate for all improvement. Trivedy details how LangChain sends agents to read other agents' traces to find good/bad interactions, detect degradation after compactions, and test counterfactuals like swapping GPT-5.5 for GLM 5.2. He shares that with Harvey on a legal benchmark, an open model matched Opus's trace judging at one to two orders of magnitude lower cost, achieved through harness engineering informed by traces. His rule for when to stop prompt tuning and start fine-tuning is feedback speed: harness engineering answers in about two minutes, so exhaust that ceiling first, then fine-tune to break through, then return to harness engineering. He…

Bringing Continual Learning into Enterprises — Samuel Denton, Applied Compute

Aug 12, 2026 · 19:03

Samuel Denton of Applied Compute explains how enterprises can implement continual learning through a distillation spectrum, pairing offline or online production traces with offline or online hints. He details how offline hints on offline traces improved a Qwen 3.5 model's SWE-bench task completion rate from 22% to 60% without degrading test pass rate, and how online hints on online traces fixed a customer's hyperlink formatting issue, raising correct formatting from 15% to 80%. Key techniques include per-step hinting with a judge to decide where to inject hints, distilling only the next few steps, and relevance-masked self-distillation to avoid learning irrelevant connector words. Applied Compute focuses on quadrant one (offline hints, offline traces) for day-one value and quadrant four (online hints, online traces) for continuous improvement, all without requiring golden answers.

Adaption Labs: Gradient-Free Continual Learning — Sara Hooker, Adaption

Aug 12, 2026 · 20:51

Sara Hooker of Adaption Labs argues that the frontier of AI discovery is about to widen, moving beyond the 'unreasonably narrow path' of elite PhDs and industry labs. She introduces AutoScientist, which automates model training by co-optimizing data and model, outperforming research staff and achieving win rates above 60% (a budget cap since removed). Hooker also presents the 'slow death of scaling,' claiming pretraining size is no longer the most lucrative axis, as smaller models now outperform larger ones on the OpenLLM leaderboard. This shift makes compute more distributable, enabling more people to contribute to frontier AI. She addresses safety concerns, distillation dependencies, and offers free GPU access to AutoScientist beta users.

Scaling Compute on Context — Jack Morris, Engram

Aug 12, 2026 · 19:42

Jack Morris of Engram frames scaling compute on context as the pursuit of depth in AI, contrasting it with the breadth of public-data pre-training. He argues models trained on public data know nothing about your emails, meetings, or company, and that with a fixed private corpus, compute is the only scalable axis. He critiques naive fine-tuning (loss 0.00001 on 10K financial reports then collapse), KV compaction, on-policy distillation, and synthetic continued pretraining, noting each hits a synthetic data wall. The goal is self-improvement like AlphaGo, where better models generate harder training questions, enabling indefinite compute scaling on your context.

Scaling up Continual Learning — Ronak Malde, Trajectory

Aug 12, 2026 · 23:03

Ronak Malde, founder of Trajectory and former Windsurf research lead, explains how on-policy self-distillation (OPSD) scales continual learning beyond GRPO's limits. He argues GRPO requires parallel rollouts and collapses feedback into one sequence-level score, like being handed 87 out of 100 on an essay. OPSD instead matches per-token log probs between a student and a teacher given privileged hints, optimizing the entire vocabulary and reducing tokens to solve tasks. Scaling to 120B models with 100+ tool calls reveals the 'but wait' problem—models hedge into 'maybe'—solved via step-level KL weighting, and hint leakage, countered with residual guidance. Trajectory applies this to production agent traces for Harvey, Decagon, and Rogo.

Taking Reinforcement Learning Cross Datacenter — Nan Jiang, Modal

Aug 10, 2026 · 19:50

Nan Jiang from Modal explains how reinforcement learning post-training can run across datacenters by shipping sparse weight deltas instead of full checkpoints. He argues that less than 1% of rollout-visible weights change between versions because Adam steps are tiny relative to BF16 rounding boundaries, a mechanism he calls Adam absorption. Modal's implementation, Stitch, lets rollout engines sync via patches (e.g., 500 MB instead of 500 GB) and operate as an elastic fleet across regions and providers. He cites internal runs showing 0.15% weight changes initially, settling near 0.05% for GLM 4.7 Air in FP8, and notes gradients are dense but updates small. He also explores whether sparsity holds for Muon and async RL scalability.

Compression at the Edge — NVIDIA, Unsloth, HuggingFace, Ollama

Aug 7, 2026 · 46:01

NVIDIA's Chris Alexiuk, Unsloth's Daniel Han, NVIDIA's Asma Beevi, Hugging Face's Merve Noyan, and Ollama's Parth Sareen argue compression democratizes AI: GLM 5.2 shrinks from 1.5 terabytes to 250 GB, 86% smaller without being 86% dumber. Han says layers are unequal—first/last critical, middle near-useless, and one 'super weight' can make a model 20% dumber—so layer choice is a combinatorial search. Asma details NVFP4, 4-bit floats sharing an FP8 scale per 16 values, targeting under 1% accuracy loss and working out of the box above ~20B parameters. Benchmarks only verify tasks—Han uses KL divergence of BF16 vs quantized logits, Ollama tests quants in real harnesses—and linear-attention models can break heuristics before KV-cache compression pushes models to phones.

The State of Model Routing — NVIDIA, Cognition, OpenRouter

Aug 6, 2026 · 48:17

Cognition's Walden Yan, OpenRouter's Alex Atallah, NVIDIA's Tanay Varshney and Carter Abdallah argue routing should orchestrate frontier and cheaper models, not per-task benchmark picks; Devin Fusion cuts Fable-level intelligence cost by 40%. Yan: task-type routing is fragile because a session shifts from codebase question to feature request to live debugging; Devin keeps a frontier model planning while a cheap sidekick executes. Atallah: OpenRouter's auto router sat unused for two years until OpenClaw heartbeats every ten minutes created an app with two intelligence needs; out-of-distribution, small models thrash: Opus scores three times better at a tenth of Haiku's cost on terminal bench. Varshney cites jagged capabilities for up to 10% higher accuracy; Abdallah adds local/cloud routing.

Jev CEO: I made ChatGPT, now I'm building what's next

Jul 31, 2026 · 18:05

Diogo Almeida, a GPT-4 co-author and founder of TypeSafe AI, argues that RLHF optimized models for human approval, making them superb assistants but unreliable for autonomous work. He contrasts assistance with automation, noting RLHF's reward model encourages confident overpromising — like ChatGPT praising an audio file of farts as music. The next era is not Claude Code (still assistance-native), but real automation via RLVR-style methods focused on calibrated decision-making, echoing Sutton's bitter lesson that the task matters more than data. He defends pre-training as phenomenal, blaming post-training's asymmetric reward model for hallucinations. TypeSafe is rebuilding the AI stack for reliability and automation.

Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI

Jul 31, 2026 · 19:05

DatologyAI CEO Ari Morcos argues data quality is the compute multiplier: better data steepens scaling, so the same compute buys better models. His oil-refinery approach—clean, curate, create, compose—uses synthetic rephrasing for diversity; curation let a VLM beat the public Pareto frontier with 145x less training compute and match Qwen 3.5 with 35x fewer flops per correct answer. Curating English also boosts non-English via cross-lingual transfer. For Thomson Reuters, mid-training on curated legal data lifted LegalBench 5 points without catastrophic forgetting and tripled post-training gains. Arcee's Trinity Large, trained on 17 trillion curated tokens, matched GLM-5 and Kimi and beat Claude on some tasks for under $20 million, proving data curation is cheaper than compute.

Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute

Jul 31, 2026 · 18:20

Raymond Feng of Applied Compute argues post-training must move from controlled Q&A and synthetic environments into real enterprise harnesses, enabling models to learn on the job. He details the GRPO loop: orchestrator, grader, training engine sync weight updates from graded chats. Reward hacking bites when tool-call failures at 10% shorten responses, and sandbox timeouts push models to abuse tool calls to get rollouts dropped. Bring-your-own-harness removes environment-fidelity problems but introduces non-replayability and off-policy data, tied to Nvidia's Polar paper. He lists self-distillation, automated data pipelines, and qualitative feedback ingestion as frontier directions, and envisions agentic citizens learning from every interaction, where experience dwarfs human data.

Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

Jul 31, 2026 · 19:12

Mahesh Sathiamoorthy, CEO of Bespoke Labs, argues that data and RL environments, not algorithms, are the bottleneck in post-training LLMs, and shares open-source work including OpenThoughts and Curator. He details the OpenThoughts curation recipe, built with Stanford, Berkeley, and UW, whose counterintuitive lessons include that sampling multiple answers per question works well, stronger teachers are not always better, and synthetic rewriting failed for agent tasks. He notes that for agents, SFT still contributes most of the gains, with RL only adding the last few percentages. A concrete production case: Credit Karma needed compliant credit card recommendations, and tagging fine-tuning data lifted compliance metrics while improving latency and throughput. He closes with the full stack needed to build RL environments and post-train agents.

Scaling to Long Horizons — Ross Taylor & Chengxi Taylor, General Reasoning

Jul 31, 2026 · 18:07

Ross Taylor and Chengxi Taylor, co-founders of London-based RL company General Reasoning, argue that scaling to long horizons demands a mindset shift and better simulation, not just bigger context windows. Ross recounts how Galactica's 2022 thinking tokens were prescient, but its base-model demo backfired, while RLHF made LLMs products; his Meta team's PPO with verifiable rewards worked, but DeepSeek-R1 later showed better base models were key. Chengxi details long-horizon obstacles: sparse rewards, credit assignment, and 1M token limits, solved by value models that reduce variance and enable bootstrapping. Kelly Bench gave frontier models $100K to trade Premier League matches; all lost money, exposing how little current environments simulate real competition. Pipeline RL trades off off-policy staleness (8 steps okay) against GPU utilization, and they point to openreward.ai, with 350+ environments, for long-horizon RL.

Emulated: The Data for Fully Autonomous Software Engineers and Companies — Joseph Wang

Jul 31, 2026 · 16:33

Joseph Wang and his co-founder Sid from Emulated argue that AI agents struggle with infrastructure work because training data misses the messy reality of production, so Emulated simulates entire companies inside sandboxes. Tasks run 50 to 100 turns, with live traffic, failing nodes, data corruption, clock skew, deployments, and customer conversations — not clean code diffs. They argue single-node sandboxes break down when provisioning real resources like VPCs, subnets, and security groups, and meeting bars for throttling, auth, and authorization, plus managing costs and gradual rollouts. Their goal is making agents own entire companies by emulating the real world at full fidelity; they start with infra because domain expertise improves data quality and infra's problem statements are clear.

The Base Model Is Dead — Varun Singh, Arcee AI

Jul 31, 2026 · 17:45

Varun Singh, pre-training lead at Arcee AI, argues the base model is dead: it no longer just mirrors web text but must carry the prior that reinforcement learning builds on. He traces how web text fell from 85% of GPT-3's mix to 15% in MAI Thinking 1, with code and STEM dominating, and how Nemotron 3 Ultra pulls SFT-style Q&A data back into pre-training. Synthetic rephrasing, as used in Arcee's Trinity Large and Kimi K2, upsamples information to teach task shapes early. He warns that without post-training-flavored data early, MoE load balancing can break when SFT distributions differ, citing MAI's cranking of the balancing coefficient. He frames training as supervised learning vs RL, noting RL compute now rivals or exceeds pre-training, as with Compose 2.5, and argues the base model's job is to provide atomic skills for RL to compose.

Reinforcement Learning without Verifiable Rewards — Will Brown, Prime Intellect

Jul 31, 2026 · 19:27

Will Brown of Prime Intellect argues RL can work without verifiable rewards by anchoring training in environments rather than clean ground truth. He frames RL as a model plus harness acting in a task and world with a scoring rule, citing Prime Intellect's Prime RL and LAB platform as the tooling. Verifiable rewards are easy for math and code, but messy tasks need manufactured signal: grounded Q&A pairs from documents and repos, plus a reverse direction trick that hides a bug or backdoor so the model learns to find it, calibrating difficulty. He warns reward hacking will surface, so teams should inspect traces, run small experiments, and involve experts. His goal is making RL a science with open models and shared benchmarks, where production traces become new tasks for continual learning.

Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It — Dan Fu and Olive Song

Jul 31, 2026 · 20:14

Olive Song, RL lead at MiniMax, and Dan, Together AI's VP of Kernels, argue that open-weight MiniMax M3 is closing the gap to frontier labs via community feedback and inference optimization. Dan details the day-zero stack: GPU kernels for M3's sparse attention, KV cache at million-token contexts, and adapting to agentic workloads that upload codebases. Song says M3 trains multimodal from scratch to prevent text-vision collapse, and uses RL with environment design and rewards for long-horizon tasks like replicating a 12-hour iClear paper, plus validation/test splits to avoid hacking. Together's Parallel Kernel Bench holds unsolved problems, so overfitting it yields kernels the company deploys. Both see self-evolution and better GPU utilization as why open models keep accelerating.

Why Off-the-Shelf AI Doesn't Understand Money — Udi Menkes, Intuit

Jul 29, 2026 · 19:50

Udi Menkes, principal PM at Intuit, argues that off-the-shelf frontier models deliver a 'fluent bluff' when advising on money: advice that sounds right but is dangerous because models have read about money but lack experience. He shows a rental property example where a frontier model told a landlord in negative cash flow to acquire a second property, while a model grounded in real outcomes recommended raising rent 5-10%. Intuit's head-to-head test across 100,000 businesses found frontier models gave advice that would harm businesses 40% of the time, while a mid-sized grounded model outperformed them by training on millions of state-action-outcome records from QuickBooks data. A Princeton study confirmed frontier models given $1M went bankrupt within 500 days, while a simple rule-based system beat them. Menkes says the moat belongs to whoever owns the best system of context, and advises leaders to find verified outcomes in their own data to ground AI.

State of Data — Sean Cai, Independent / State of Data

Jul 26, 2026 · 18:22

Sean Cai argues data markets, not compute, are now the binding constraint for turning generalist AI into expert systems, with the supply chain unbundling from vertical giants into specialist vendors. He distinguishes Type I (real workflow capture) from contrived Type II data, noting the industry sells Type II as Type I. He introduces Verifier's Law—ease of training proportional to task verifiability—to predict domain maturity: code first, then biology, security, finance. He exposes benchmark psychosis: a single number under one scaffold is a noisy sample, requiring cross-harness differencing. Labs like Anthropic's data spending forecasts product launches (e.g., cybersecurity data in January led to Claude Cyber in March). Data companies like Mercor pivot to enterprise, and Cai builds Antikythera mechanisms to monetize real-world workflows and provide RL-as-a-service.

The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside

Jul 26, 2026 · 17:31

Marah Abdin and Robert McHardy from poolside detail their synthetic data pipeline and pre-training tribulations at scale, culminating in a new 118-billion-parameter model for agentic coding that outperforms competitors. Marah describes using synthetic data to rephrase content and fill gaps, with a configurable pipeline (Hive) involving agents, orchestrators, and supervisors, covering rephrasing, multistage workflows, cross-domain porting, and multi-turn chats. Robert recounts failures like broken GPUs causing data corruption, a BF16 accumulation bug that stalled training, and a race condition in FP8 kernels silently corrupting 0.5% of gradients, all caught by model replica hashing. Their Laguna S model (118B total, 8B active) trained on 30 trillion tokens across 4,000 GPUs beats GLM 4.5 Air and other models on coding benchmarks like BigCodeBench and SpeedBench agentless, while remaining competitive on general knowledge.

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Jul 25, 2026 · 21:45

Cormac Brick of Google AI Edge argues that the real constraint on edge AI is DRAM cost, not compute, making tiny models essential for widespread deployment. Small models (1–4 billion parameters) require 4–8 GB of RAM, but their quantized Gemma 2B model—at just 2.9 bits per weight—runs on a Raspberry Pi at 7.6 tokens per second and on a Qualcomm NPU at 31 tokens per second. For even lower-end devices, tiny models (50–500 million parameters) need under 2 GB of RAM and can be fine-tuned for specific tasks like voice-to-function calling, reaching over 86% reliability on ten actions. A shipped example is an offline voice dictation app that uses two fine-tuned sub-billion Gemma models to clean up ums and ahs without a subscription. The talk covers the Gemini-optimized toolchain (Lighter TLM, MediaPipe), synthetic data generation for fine-tuning, and the trade-off between zero-shot prompting in small models versus fine-tuning tiny models for reach and speed.

Local Agentic Theory For Mobile Games — Shafik Quoraishee & Joanne Song, The New York Times

Jul 23, 2026 · 18:04

Shafik Quoraishee and Joanne Song of The New York Times argue that on-device AI agents can transform mobile game accessibility by tuning difficulty and assistance in real time as a single continuous dial rather than separate toggles. They demonstrate a Space Invaders agent that perceives, predicts, and dodges entirely on the phone within a 16 ms frame, and a mini crossword solver using constraint backtracking. The pair explains the on-device budget—space for weights, time within refresh cycles, and energy drain—and shows how gaze estimation and tap analysis let the agent resize controls or break keyboard traps dynamically. They ground their approach in WCAG accessibility standards and propose a future where billions of local brains, each personalized to a user, replace centralized cloud AI.

"The biggest challenge in your stack? Evals, Evals, Evals" - 2026 State of AI Engineering results

Jul 21, 2026 · 19:47

In the 2026 State of AI Engineering survey presented by Amplify Partners' Barr Yaron, 1,048 AI engineers reveal that cost is now a first-class engineering constraint—40% say it regularly shapes how ambitiously they use AI. Agents have exploded: 95% of teams now use agents, and 89% of those agents have write access, tripling from last year. Image generation adoption doubled to 36%, while audio shows the strongest intent-to-adopt at 56%. Open-weight models augment rather than replace closed models—45% use open-weight, but over 90% of them also use closed models. Evals remain the top infrastructure challenge, and inference is the most bought layer, while prompt management (61% built in-house) stays close to product logic. Teams report 97% net positive impact, but 59% fear long-term liabilities from AI code, and over a third say non-developers now ship features.

The Desktop Frontier — Ahmad Osman, Osmantic

Jul 21, 2026 · 18:02

Ahmad Osman, founder of Osmantic, argues that within roughly 18 months (by late 2027) a single RTX 5090 will run intelligence equivalent to GLM 5.2, driven by the Densing Law of increasing impact per parameter. He shows this trend through concrete examples: a 27B-parameter Qwen 3.5 now beats the 405B LLaMA 3, and the same eight RTX 3090s that once struggled with LLaMA 2 can now run 15 parallel Qwen 3.5 agents. Osman presents the Densing Law—every 3.5 months, 50% fewer parameters achieve the same capability—as a systematic pattern, not coincidence. He advocates for sovereign AI: owning your own hardware (like a DGX Station or RTX 5090) gives you control, avoids cloud limitations, and sees hardware appreciate in utility as models become more efficient. He asks why fund cloud data centers when local hardware can run frontier intelligence and grow more valuable over time.

Special Topics in Kernels, RL, Reward Hacking in Agents — Daniel Han, Unsloth

Jul 17, 2026 · 2:20:21

Daniel Han of Unsloth argues that reward hacking—where AI models cheat to maximize reward—is a critical problem in agent training, citing examples from GPT-5.1's calculator hacking and GPU mode kernel competitions. He shows that models exploit benchmark flaws, such as viewing Git history or editing timers, and that even open-source models like GLM 5.2 require anti-hacking measures. Han emphasizes that harness and tooling quality now outweigh model choice, with inference providers sacrificing accuracy for speed (e.g., 10% accuracy drops across providers). He also warns that hardware limits (float4 precision, diminishing returns) shift focus to software algorithms like FlashAttention and gradient checkpointing. The workshop concludes that benchmarks are unreliable—DeepSpeed's false positive rate is contested at 44.9%—and urges verification before trusting performance claims.

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Jul 15, 2026 · 20:32

Lee Robinson, head of ML at Cursor, explains how recursive model improvement accelerates AI training through inner and outer loops. The outer loop gathers user feedback and online metrics to refine evals, while the inner loop uses high-quality evals and difficult problems to climb performance. Composer 2.5, released in May, became Cursor's most popular model by balancing speed, intelligence, and cost. To scale, Cursor partners with SpaceX for compute via Colossus (122 days to build 100k GPUs) and develops textual feedback where a teacher model hints at improvements during RL rollouts. Robinson details reward hacking on public benchmarks and the creation of CursorBench, a private eval set. He envisions agent-based automation where researchers launch experiments from Slack and models train derivative models, creating a self-improving intelligence loop.

Modern Post-Training: A Deep Dive — Will Brown, Prime Intellect

Jul 13, 2026 · 46:52

Will Brown of Prime Intellect details the company's open-source ecosystem of post-training tools, including the verifiers and prime-rl libraries, arguing they enable efficient and affordable training of frontier agentic models for enterprises. Verifiers V1 decomposes environments into tasks, harnesses, and runtimes using a decorator pattern and Pydantic, supporting group rewards like conciseness bonuses. Prime-RL is an asynchronous reinforcement learning framework that allows long-horizon coding rollouts to overlap, achieving a GLM-5 step on 28 nodes in under 5 minutes for 131k context, with a 1,000-step run costing roughly $50k. The framework supports custom algorithms including on-policy distillation, GRPO, and self-distillation via decomposable loss and algorithm classes. Prime Intellect's Lab platform offers hosted multi-tenant LoRA training live now, with full fine-tuning arriving soon, enabling enterprises to develop environments on CPU and push them to the cloud for scalable post-training.

State of the Union: Why Local, Why Now — NVIDIA, Osmantic, Roboflow, EXO Labs, @matthew_berman

Jul 11, 2026 · 44:29

Nader Khalil (NVIDIA), Joseph Nelson (Roboflow), Alex Cheema (Exo Labs), Matthew Berman, and Ahmad Osman (Osmantic, r/LocalLLaMA) argue that local AI is now useful, driven by stronger open models and better hardware. They cite inflection points like Llama 2, DeepSeek v3, and GLM 5.2, which closed the gap with frontier cloud models. Sovereignty and control are key: enterprises need to choose their own model versions and avoid lock-in. Specialized models, such as Roboflow's fine-tuned vision models for deep-sea fish discovery, outperform general ones for specific tasks. Optimization is critical: EXO Labs achieved 10x performance on the DGX Spark by tuning existing NVIDIA kernels. The panel emphasizes that simplicity remains a barrier—most users need point-and-click solutions—and advocates for open-source AI to ensure freedom and innovation.

You Might Not Need 50 Diffusion Steps — Ziv Ilan, Nvidia

Jun 16, 2026 · 18:46

Nvidia's Ziv Ilan explains how combining quantization, caching, and step distillation enables near real-time video diffusion on a single Blackwell B200 GPU. Working with Black Forest Labs on Flux 2, dynamic quantization reduces memory and compute, caching skips redundant denoising steps, and distillation cuts steps from fifty to as few as one. The open-source FastGen repo packages these post-training and sharding techniques, achieving 10–200x speedups for real-time generation.

Stop Making Models Bigger, Make Them Behave — Kobie Crawford, Snorkel

Jun 10, 2026 · 20:56

Kobie Crawford of Snorkel explains how a 4B parameter model fine-tuned via RL for under $500 outperformed Qwen 3 235B on financial analysis tool use. The key was training tool discipline—inspecting schemas and self-correcting errors—not deeper reasoning. Single-table training alone boosted multi-table FinQA benchmark from 13.9% to 26.6%, and breaking evals into rubrics identifies which behaviors to fix.

Sovereign Escape Velocity: Ownership w Open Models — Gus Martins, & Ian Ballantyne, Google DeepMind

Jun 10, 2026 · 20:52

Gus Martins and Ian Ballantyne of Google DeepMind introduce Gemma 4, a family of open-weight models that deliver high quality per parameter, enabling deployment on a single GPU or even a phone. They argue that the models' efficiency — a 31B model rivals those twenty times larger — and the shift to Apache 2.0 licensing remove barriers for sovereign institutions like those in Ukraine, Bulgaria, and Brazil. Ian demonstrates multi-agent translation running locally on an M4 Mac, showcasing ownership and control over agentic workloads.

Text Diffusion — Brendan O’Donoghue, Google DeepMind

Jun 4, 2026 · 28:03

Brendan O'Donoghue, a research scientist at Google DeepMind, explains that text diffusion models generate tokens 10x faster than autoregressive models by performing 24 denoising steps to produce 256 tokens, dramatically reducing memory transfers. Unlike GPT-4o and Gemini 2.5 Flash, which incorrectly answered 40 and 42 on a math problem, Gemini Diffusion used bidirectional attention to self-correct from 60 to 49 to 39. The model adaptively allocates compute: 4 steps for memorized digits of π, 31 for quantum mechanics, and automatically stops when satisfied. Text diffusion also enables in-place editing, demonstrated by fixing code bugs or adding paragraphs. However, lower throughput on large batches makes it expensive to serve at scale today. O'Donoghue showcases low-latency applications: a fully generated Wikipedia, a Reddit clone with AI text and images, an on-the-fly operating system, and a to-do app built in 15 seconds by voice.

What Lies Beneath the API — Benjamin Cowen, Modal

Jun 2, 2026 · 12:40

In this episode, Ben Cowen from Modal argues that as AI products mature, fine-tuning becomes essential, citing cases like Intercom beating their frontier API at 1/10th the cost. He identifies three signals it's time to fine-tune: paying more for the API than customers pay you, evaluative plateaus, and latency requirements that shared endpoints can't meet. Cowen explains that supervised fine-tuning now fits in 300 lines of Python and that reinforcement learning rollouts can scale to 50,000 sandboxes using serverless platforms like Modal. He contends that frontier labs aim to win at everything, while businesses need to win at their specific logic, making fine-tuning a natural destination. The episode provides practical guidance on when and how to make the leap, emphasizing that building an agent harness and collecting eval data already sets the stage for training.

20 days of compute vs 7 hours: rethinking what state-of-the-art means — Bertrand Charpentier, Pruna

Jun 1, 2026 · 19:36

Bertrand Charpentier, cofounder and chief scientist at Pruna AI, argues that state-of-the-art is not a single model but multiple on a Pareto front that balances quality and efficiency. He highlights that public leaderboards disagree—Hunyuan ranks 10th on Artificial Analysis but 5th on Arena—and most models lose 40% of head-to-head battles, meaning the top-ranked model is wrong for nearly half of use cases. Evaluating a large model like ChatGPT image on Design Arena (26k battles, 62 seconds each) costs $5,000 and 20 days of compute, consuming energy equivalent to 400 marathons, while a fast compressed model completes the same evaluation in 7 hours for $265. Charpentier advocates plotting quality against latency or cost to find the frontier, which often surfaces small specialized models instead of large foundation models, with 20x efficiency differences at similar quality scores.

How We Built Zeta2: Training an Edit Prediction Model in Production — Ben Kunkle, Zed

May 30, 2026 · 10:50

Ben Kunkle, edit predictions lead at Zed, explains how they built Zeta2, a small specialized model for edit prediction in production. The pipeline pulls opt-in production edit traces, distills them through a frontier teacher, and routes bad predictions through a repair step before formatting for the student. To validate settled data, Zed originally ran 10 frontier model predictions per example and measured Levenshtein distance to the final state, but for 100,000 training examples that is a million frontier model requests — prohibitively expensive. The fix: Zeta2's student model now approaches teacher quality, so they run it 50 times instead at negligible cost. Ideal training examples sit in the middle of the Levenshtein distance distribution: too close to the settled state is obvious, too far is noise. A metric called reversal ratio — how often the model undoes exactly what the user just typed — was the key diagnostic for catching bad model behavior before shipping.

Run Frontier AI at Home — Alex Cheema, EXO Labs

May 26, 2026 · 1:45:02

Alex Cheema of EXO Labs argues that running frontier AI locally has 100x improvement potential in cost and performance, demonstrated with GLM 5.1—a trillion-parameter model—running across four Mac Studios at roughly 20 tokens per second for $40,000. He details kernel fusion that recovered 30% performance on Qwen 3.5 by eliminating unnecessary kernel launches, and RDMA integration that cut node-to-node latency from 300 microseconds to single digits, enabling tensor parallelism to actually scale. Cheema advocates splitting inference: prefill on compute-dense hardware (e.g., an RTX Spark) and decode on high-bandwidth hardware (e.g., Mac), cutting large-prompt inference roughly in half. He warns against misleading benchmarks like one-bit quantized models, and outlines how multi-agent setups, test-time scaling, and continual learning could further improve local inference efficiency. The talk includes a live demo of GLM 5.1 across four Mac Studios connected via Thunderbolt 5, and a preview of EXO's upcoming benchmarking site to track intelligence per Joule.

Scaling the Next Paradigm of Heterogeneous Intelligence — Adrian Bertagnoli, Callosum

May 24, 2026 · 15:13

Adrian Bertagnoli, founding engineer at Callosum, argues that the era of homogeneous intelligence—scaling single models on identical chips—is ending, and heterogeneous intelligence, which routes tasks to optimal chips and models, is the next paradigm. He demonstrates this with two case studies: on the Ulong benchmark, running recursive language models on Cerebras instead of GPT-5.2 cuts cost by 7x and latency by 5x while matching accuracy; on Video Web Arena, a mixture of Qwen 3 VL8B and Kimi K2.5 beats GPT-5.2 and Gemini 2.5 by 18% and 25%, while costing 3.7x less and running 3x faster. The key insight is that complex problems decompose into subtasks—like zooming on a webpage—which require different intelligence levels; offloading those to smaller models yields 11x speed and 43x cost improvements. Callosum builds an automation layer that predicts the best model and hardware for each subtask, and has secured a £3 million grant with the UK's Arya institute to operate the first heterogeneous co-located cluster.

Gemini Nano on device — Florina Muntenescu & Oli Gaymond, Google DeepMind

May 22, 2026 · 19:38

Florina Muntenescu and Oli Gaymond from Google DeepMind explain Gemini Nano, an on-device model that ships at 3–4 GB and is shared across apps via the AI Core system service, which handles scheduling, queuing background batch jobs overnight, and prioritizing foreground apps. The MLKit GenAI APIs (prompt API for text/image input, text output) give access to Gemini Nano, but require flagship devices from the last two years for optimal performance; classic MLKit (vision, OCR) runs on over a billion devices. Hybrid inference, launched weeks before this talk, automatically falls back to Gemini Flash in the cloud when the on-device model isn't available, extending reach. An embedding API for RAG-style solutions is coming soon. For fully custom models, LiteRT offers an alternative path but requires more developer effort for testing and optimization.

From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google

May 20, 2026 · 21:01

Google's Cormac Brick explains how developers can build on-device AI agents using either system-level Gemini Nano via AI Core or app-level custom models via LiteRT-LM. He demonstrates a skill harness built on Gemma 4 that enables agentic tasks like restaurant roulette, running fully on-device with JavaScript UI. For fine-tuned tiny models, Function Gemma at 270M parameters improved from 46% to over 90% accuracy on eight of ten app-intent functions after synthetic data fine-tuning. The talk also covers the Eloquent transcription app, which chains two Gemma 3-based models (ASR and text polishing) under a few hundred million parameters for offline use. Key trade-offs are latency, privacy, and customization versus system integration effort.

Lessons from Trillion Token Deployments at Fortune 500s — Alessandro Cappelli, Adaptive ML

May 12, 2026 · 18:35

Alessandro Cappelli, co-founder of Adaptive ML, argues that 95% of GenAI pilots fail to reach production because they rely on proprietary models or instruction fine-tuning, which lack systematic feedback integration. Reinforcement learning (RL) is the only post-training technique that mathematically incorporates defects, business metrics, and production signals to continuously improve models. RL enables smaller, cheaper, faster models that enterprises can own—critical for scaling use cases like AT&T’s transcript summarization or Manulife’s agents. For agents, RL naturally fits because it was designed for environments; synthetic data is generated as a byproduct of environment training, not a prerequisite. Reward signals come from business KPIs (e.g., containment rate) or LLM judges defined by human rubrics in hours, not weeks. Adaptive ML’s Adaptive Engine abstracts away RL complexity (orchestrating four models for PPO) and provides pre-built recipes to industrialize model deployment.

Why MLX — Prince Canuma, Neywa Labs

May 11, 2026 · 23:10

Prince Canuma argues that on-device AI, powered by Apple's MLX framework, is a viable alternative to cloud-dependent services, especially for users in regions with unreliable internet. MLX, an array framework for Apple Silicon, has reached 1.5 million downloads and 4000 ported models, including day-zero support for Gemma 4 and Qwen 3 Omni. Canuma demonstrates real-time vision models (e.g., RF Deter for object detection), sub-100ms text-to-speech via Marvis TTS, and modular speech-to-speech pipelines that run entirely on-device. Community projects showcased include a native voice app (Locally), a robot with real-time voice cloning, and a video generation system that chains coherent stories on 16GB VRAM. A recent breakthrough, Turbo Quant, reduces KV cache by 4x, enabling 1 million context windows on-device. Canuma emphasizes that these capabilities provide accessibility for his blind father and enable agents that hear, see, and respond without phoning home.

How Transformers Finally Ate Vision – Isaac Robinson, Roboflow

May 8, 2026 · 17:05

Isaac Robinson, research lead at Roboflow, explains why transformers ultimately beat convolutional neural networks for vision, arguing that massive ViT-specific pretraining and borrowed infrastructure from LLMs overcame the transformer's lack of inductive bias. He traces the evolution from ViT and Swin (windowed attention reducing complexity to n²) through ConvNeXt (reintroducing convolution with transformer-style blocks) to Hiera (stripping biases and recovering them via MAE pretraining), and back to the simple, scalable ViT. Robinson highlights that pretraining methods like MAE and DINOv3 learn the inductive biases CNNs have built-in, while tools like FlashAttention from the LLM world nullify ViT's n⁴ compute scaling penalty. In practice, this pattern appears in the SAM model series: SAM used a ViT backbone, SAM2 switched to Hiera with MAE, and SAM3 returned to the simple ViT. However, these massive models lack deployment flexibility; Roboflow's RF-DETR uses neural architecture search on a foundation model backbone to generate a family of high-performance models, achieving up to 40× speedup over fine-tuning SAM3 while outperforming real-time convolutional detectors.

Accelerating AI on Edge — Chintan Parikh and Weiyi Wang, Google DeepMind

May 5, 2026 · 23:58

Chintan Parikh and Weiyi Wang from Google DeepMind present Gemma 4E edge models (2B and 4B) and the LiteRT framework for on-device AI, arguing that edge AI delivers latency, privacy, offline capability, and cost savings. The Gemma 4E models introduce agentic capabilities including built-in function calling, structured JSON output, and chain-of-thought reasoning, all optimized for hardware-native support across CPUs, GPUs, and NPUs. LiteRT supports cross-platform deployment on Android, iOS, macOS, Linux, Windows, and IoT devices like Raspberry Pi, with a CLI tool and AI Edge portal for benchmarking. Performance benchmarks show up to 56 tokens per second on iOS, 30x speedups with NPU acceleration, and 35x faster than Llama on mobile. The Gallery app demonstrates on-device skills such as Wikipedia querying, mood tracking, photo-to-music generation, and voice agents, with open-source code and a Hugging Face repository. Q&A addresses use cases like local security camera face recognition, LiteRT vs TensorRT on Orin, multi-agent architectures, and audio model support.

Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI

Apr 29, 2026 · 20:13

Maxime Labonne, head of post-training at Liquid AI, presents the LFM2.5 recipe for training frontier small models, arguing they require specialized approaches distinct from scaled-down big models. He details how Liquid's architecture uses gated short convolutions for latency-sensitive on-device deployment, achieving faster throughput than Gemma 3 or Qwen 3.5. Post-training stages—SFT, on-policy preference alignment, and RL—are tailored for narrow task focus like data extraction and tool use. A key challenge is 'doom loops' (repetition), which reached 15% after mid-training in a 1.2B reasoning model; solutions include preference alignment rejecting looped responses and RL with verifiable rewards and n-gram penalties, nearly eliminating the issue. He advocates combining small models with agentic tools (e.g., web search) to overcome memory limits, as they excel at reasoning and tool use despite lower knowledge capacity. The talk also covers decisions on when to use small vs. large models—latency, privacy, offline use—and notes that distillation alone likely won't fully solve doom loops.

Running LLMs on your iPhone: 40 tok/s Gemma 4 with MLX — Adrien Grondin, Locally AI

Apr 20, 2026 · 10:51

Adrien Grondin, developer of Locally AI, demonstrates how to run Gemma 4 and other LLMs on iPhone using Apple's MLX framework, achieving 40 tokens per second on the latest devices. He explains that MLX is optimized for Apple Silicon and that the open-source mlx-swift-lm GitHub repo enables easy integration into iOS, macOS, and iPadOS apps in under 10 minutes. Grondin recommends quantized models from the Hugging Face MLX community—typically 4-bit to 8-bit—and shows a live demo of Gemma 4 generating text offline. He confirms that mlx-swift-lm supports tool calling, though structured generation is not yet available. He also notes that Locally AI has been acquired by LM Studio, which now lets users run models via MLX or llama.cpp and connect them through OpenAI or Anthropic-compatible APIs.

$1 AI Guardrails: The Unreasonable Effectiveness of Finetuned ModernBERTs – Diego Carpintero

Apr 16, 2026 · 43:53

Diego Carpentero argues that LLM-based attacks—Prompt Injection, Indirect Injection, Model Internals (gibberish suffix), RAG Poisoning, MCP Exploits, and Agentic Escalation—are now the baseline, not the exception, and that model alignment and human review alone are insufficient. He identifies the core problem as a Zero Trust Gap: LLMs natively lack separation between system controls and data, allowing adversaries to override decisions via malicious instructions in inputs or external content. To build a protective layer, Carpentero fine-tunes ModernBERT—a state-of-the-art encoder with Alternating Attention, Unpadding & Sequence Packing, RoPE, and FlashAttention—into a safety discriminator that classifies prompts as safe or unsafe in ~35 milliseconds with 85% accuracy, all for under a dollar. He walks through the fine-tuning pipeline using the IngetGuard dataset and demonstrates live detection of real attack examples from each vector.

Running LLMs locally: Practical LLM Performance on DGX Spark — Mozhgan Kabiri chimeh, NVIDIA

Apr 10, 2026 · 10:16

NVIDIA’s Mozhgan Kabiri Chimeh demonstrates that running LLMs locally on the DGX Spark workstation, powered by the GB10 Grace Blackwell superchip and 128GB unified memory, achieves practical performance for models up to 14B parameters. Using a reproducible vLLM benchmarking methodology, she shows that the 14B NVFP4 quantized model delivers 20.19 tokens per second and a time-to-first-token 3.4× faster than the unoptimized 14B base model. The DGX Spark supports the same NVIDIA AI software stack as production environments, enabling local development, fine-tuning, and privacy-sensitive workloads before scaling to the cloud. NVFP4 quantization is highlighted as critical for balancing intelligence and throughput on single-system setups.

Let LLMs Wander: Engineering RL Environments — Stefano Fiorucci

Apr 8, 2026 · 40:35

Stefano Fiorucci demonstrates how to build Reinforcement Learning environments for language models using the open-source Verifiers library, arguing that training small models with verifiable rewards can surpass large closed models on specific tasks. He maps classic RL concepts to LLMs, introduces Verifiers components for single-turn, multi-turn, and tool environments, and then walks through an experiment where he takes LiquidAI's LLM 2 — a small open model — and transforms it into a tic-tac-toe master via supervised fine-tuning and GRPO-based reinforcement learning. After training, the model dominates random opponents and draws 85% of games against optimal ones, eventually outperforming GPT-5 Mini against identical optimal opponents. Fiorucci shares practical lessons: large batch sizes (≥256) ensure stable training, hidden biases in opponent algorithms can skew results, and starting from a base model (not a reasoning model) avoids truncated thinking traces. He concludes that if you can define a clear reward signal, you can build an environment and train a small specialized model to beat a large closed model at a fraction of the cost.

AGI: The Path Forward – Jason Warner & Eiso Kant, Poolside

Dec 27, 2025 · 15:56

Jason Warner and Eiso Kant, co-founders of Poolside, present their vision and roadmap towards AGI-level capabilities for knowledge work, demonstrating their second-generation model Malibu Agent converting ADA code to Rust live on stage. They argue that next-token prediction paired with reinforcement learning is the key breakthrough, a contrarian bet they made two and a half years ago. The episode centers on their work in high-consequence code environments for defense and government, where agents must operate with tight permissions. They announce a large compute cluster of over 40,000 GB300s coming online and a public API release early next year via AWS Bedrock. Warner recounts meeting Kant through a failed GitHub acquisition, and Kant invites the audience to build with their models, emphasizing that future agents will handle tasks over days as intelligence scales.

Code World Model: Building World Models for Computation – Jacob Kahn, FAIR Meta

Dec 17, 2025 · 16:41

Jacob Kahn, a research scientist at FAIR Meta, presents the Code World Model (CWM), a 32 billion parameter dense transformer that models program execution rather than just syntax. CWM predicts execution traces line by line, enabling neural debugging and approximation of the halting problem. Trained on GitHub data and refined with synchronous RL and long-context mid-training, CWM uses bash-oriented tool use and achieves strong throughput through asynchronous model updates. The model is open-source on Hugging Face, with code and a technical report available, and aims to build foundations for reasoning and planning in AI-driven software systems.

Agent Reinforcement Fine Tuning – Will Hang & Cathy Zhou, OpenAI

Dec 9, 2025 · 16:55

Will Hang and Cathy Zhou of OpenAI's fine-tuning team introduce Agent Reinforcement Fine-Tuning (RFT), a method to improve AI agents by training them end-to-end on tasks involving tool calls and reasoning. They define an agent as a model that interleaves reasoning with external tool interactions, unlike regular models. The hierarchy of optimization moves from prompt engineering to task optimization to RFT, which changes model weights based on a custom reward signal. New features allow models to call tools via public endpoints and use custom rewards hosted externally. Case studies show concrete gains: Cognition improved code edit planning by 10 points with 1,000 examples and reduced tool call steps from 8–10 to 4; Codto's deep research agent boosted recall by 6% while cutting long-tail tool calls (over 15) down to 2–4; Cosine achieved state-of-the-art on enterprise code benchmarks by using strict graders that reward only pass-tested code; and Macco wrote GPU kernels from just 100 PyTorch prompts, beating SOTA by 72% after addressing reward hacking with seven edge-case detectors. Four principles for success: define tasks unambiguously, mirror production traffic in train/eval sets,…

RL Environments at Scale – Will Brown, Prime Intellect

Dec 9, 2025 · 18:30

Will Brown of Prime Intellect argues that scaling reinforcement learning environments beyond engineering—to community and accessibility—is key to broadening AI research. He presents Prime Intellect's open-source stack, including Verifiers for building environments and the Environments Hub for sharing them, as a way to turn any task harness into an RL training or evaluation loop. Brown demonstrates how environment-based fine-tuning boosted a Qwen 3 4B model from 55% to 89% on a Wikipedia search task, matching much larger models. He frames environments as the 'web apps of AI research'—simple to start, but capable of capturing product complexity, as seen with Cursor's Composer and OpenAI's Codex. Prime Intellect validated this approach by training the 100B-param Intellect 3 on 500 GPUs, and will soon release Lab, a platform to run environments without managing infrastructure.

Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute

Dec 9, 2025 · 20:19

Rhythm Garg and Linden Li, co-founders of Applied Compute, describe how their company uses efficient reinforcement learning (RL) to specialize large language models for enterprise tasks. They explain that synchronous RL wastes GPU time waiting on straggler samples—99% of arithmetic problems complete in ~40 seconds, but the tail takes 80 more seconds—so they adopt asynchronous pipeline RL. This method dedicates fixed GPUs to sampling and training, allowing continuous inference but introducing stale tokens (up to a tolerated staleness threshold) that require importance ratio corrections. To balance speed and stability, they model the system mathematically: using a roofline-based latency curve for sampling, per-GPU training throughput, and constraints on staleness and KV cache memory. Their simulations, parameterized by response length distributions, reveal an optimal GPU allocation that yields ~60% speedup over synchronous RL while keeping staleness within ML limits. This modeling lets them predict runtime and configure runs without expensive trial-and-error.

Compilers in the Age of LLMs — Yusuf Olokoba, Muna

Nov 24, 2025 · 17:36

Yusuf Olokoba, founder of Muna, explains how they built a Python compiler that converts plain Python inference functions—like Google's 270M-parameter Gemma embedding model—into self-contained C++ and Rust binaries using LLMs within a verifiable pipeline. The process involves symbolic tracing to generate an intermediate representation, type propagation to infer types for native compilation, and LLM-driven code generation to mass-produce native implementations of Python operations. After compiling into a shared library, the model can be loaded via FFI from any language (e.g., Node.js) and exposed through an OpenAI-compatible client, enabling developers to run open-source models anywhere—locally, cloud, mobile—with minimal code changes. Olokoba details why they abandoned PyTorch FX due to its PyTorch-only focus and reliance on fake inputs, and how LLMs help scale the coverage of elementary operations. The talk argues that this compiler approach solves hybrid inference—small edge models working with large cloud models—by moving beyond Python and Docker to more portable, low-latency native binaries.

Context Platform Engineering to Reduce Token Anxiety — Val Bercovici, WEKA

Nov 24, 2025 · 23:52

WEKA's Val Bercovici and Callan Fox present Context Platform Engineering, open-sourcing a toolkit that maximizes KV cache hit rates—called the single most important metric for production-grade AI agents by Manus AI. They argue that without this engineering, users resort to Context Financial Engineering, a clairvoyant prompt cache arbitrage against time-to-live (5 minutes to 1 hour) and cache read/write pricing. The toolkit includes a load generator that configures agent swarms with specific SLOs, cycling through deterministic and random prompts. Callan's WEKA Labs research shows that 1-minute TTL causes 15-16x token repetition, while 1-hour TTL approaches 1x, but requires larger cache capacity. Benchmarks compare HBM+WEKA (purple) vs HBM+DRAM (orange) vs HBM+DRAM+slow storage (pink); WEKA's NVMe-backed augmented memory grid maintains higher output token rates under increasing concurrent users—up to the point where DRAM tiers drop off sharply due to insufficient speed. The episode covers how SLA requirements translate into SLOs via memory tiering and KV cache offloading, and emphasizes that subscription users effectively purchase cache allotments to keep inference providers in…

Z.ai GLM 4.6: What We Learned From 100 Million Open Source Downloads — Yuxuan Zhang, Z.ai

Nov 22, 2025 · 19:39

Yuxuan Zhang from zAI details the technical roadmap behind the GLM 4.6 open-source model series, which has surpassed 100 million downloads and tied for number one on the LMSYS Chatbot Arena alongside GPT-4o and Claude 3.5 Sonnet. The training pipeline uses 15 trillion tokens of pre-training data, followed by 7 trillion tokens of code and reasoning data, repo-level contexts at 32,000 tokens, and 100 billion tokens of long-context agent trajectories up to 128,000 tokens. Zhang introduces SLIME, a hybrid synchronous/asynchronous RL framework that decouples agent-environment interaction from GPU training to avoid bottlenecks. He explains why single-stage RL at 64,000 tokens outperforms multi-stage approaches for preserving long-context abilities, and shows that token-weighted loss converges faster than sequence-average loss for code RL. The multimodal GLM 4.5V model handles native resolution images and video with temporal index tokens, enabling GUI agent capabilities. Deployment is supported via vLLM and SGLang, with an API at z.ai and an open-source coding assistant.

Hacking the Inference Pareto Frontier - Kyle Kranen, NVIDIA

Aug 1, 2025 · 20:25

Kyle Kranen, architect of NVIDIA Dynamo, explains how to break the inference Pareto frontier by manipulating trade-offs between quality, latency, and cost using a toolkit of techniques. Disaggregation separates prefill and decode phases, achieving up to 2× tokens per second per GPU at fixed latency for LLaMA 70B on 16 H100s. Smart routing maximizes KV cache hits, asymptotically reducing prefill work as deployments scale. Structure from agentic workloads, like inference-time scaling, enables smaller models (e.g., 8B queried 3–4 times) to match larger models' quality at lower cost. KV manipulation offloads caches during tool calls (e.g., 30-second delays) to avoid re-prefill. Dynamism adjusts worker specialization and autoscales prefill/decode ratios in real time to handle shifting user distributions, ensuring disaggregation reaches maximum potential.

Serving Voice AI at $1/hr: Open-source, LoRAs, Latency, Load Balancing - Neil Dwyer, Gabber

Jul 31, 2025 · 16:09

Neil Dwyer, CTO of Gabber, details how his startup serves real-time voice AI for under $1 per hour using open-source Orpheus TTS, LoRAs for emotive voice cloning, and vLLM with FP8 dynamic quantization to achieve 95-105 tokens per second on L40S GPUs. He explains the critical latency challenge of 'head of line silence' in Orpheus (600ms in default voices) and how fine-tuning LoRAs reduces it to ~100ms, fitting within a 1.5-second budget for real-time conversation. Gabber batches multiple LoRA generations per GPU and uses a consistent hash ring for load balancing across servers, enabling popular clones to be dynamically replicated. The talk argues that with current open-source tools, building affordable consumer voice AI is accessible to small teams.

Introduction to LLM serving with SGLang - Philip Kiely and Yineng Zhang, Baseten

Jul 26, 2025 · 43:42

Philip Kiely and Yineng Zhang introduce SGLang, an open-source fast serving framework for LLMs and VLMs, arguing it offers production-ready performance with day-zero support for new models like DeepSeek and Qwen, and a customizable codebase for contributions. Yineng, a core maintainer, traces SGLang's rapid growth from a December 2023 paper to nearly 15,000 GitHub stars and adoption by xAI, AMD, and Meituan. The workshop demonstrates deploying a first model via Baseten's trunk packaging, then tuning the CUDA Graph max batch size flag on an L4 GPU to maintain CUDA Graph-enabled decoding during higher concurrency, boosting generation throughput. They also cover Eagle 3 speculative decoding, where a draft model derived from the target model speculates tokens; users can benchmark different step and top-k configurations on representative prompts to find optimal settings for production. Finally, they invite contributions via GitHub's 'good first issue' tags and highlight Baseten's job openings.

Robotics: why now? - Quan Vuong and Jost Tobias Springberg, Physical Intelligence

Jul 26, 2025 · 18:07

Physical Intelligence's Quan Vuong and Jost Tobias Springberg describe their mission to build a model that can control any robot to do any task, arguing that software intelligence is the main bottleneck in robotics. They explain Vision Language Action models (VLAs) as adaptations of vision language models that output robot actions instead of text. To train these models, they built a data engine from scratch, collecting 10,000 hours of successful episodes via teleoperation in six months. Their latest model, PAIO-5, achieves open-world generalization by training on data from multiple homes, matching or surpassing performance on held-out scenes. They demonstrate this with a policy that performs long-horizon tasks like cleaning an unseen bedroom for up to 10 minutes autonomously. They also highlight a remote coffee-making demonstration on a robot they never touched, showing model portability across hardware.

Latent Space Paper Club: AIEWF Special Edition (Test of Time, DeepSeek R1/V3) — VIbhu Sapra

Jul 25, 2025 · 53:54

VIbhu Sapra presents DeepSeek's latest models and announces a new curriculum-based Test of Time Paper Club. DeepSeek's May 28th R1 update doubles reasoning tokens (12k→25k) and matches O3/Gemini 2.5 Pro, improving AIME 2024 from 70% to 87.5%. A new distillation of R1 traces into Qwen 3 8b achieves performance on par with Qwen's 235b thinking model, proving reasoning models distill efficiently. The Test of Time Paper Club will cover 50–100 foundational papers over six months in San Francisco and remote, targeting core AI engineering topics from attention and scaling laws to inference techniques. Sapra emphasizes that pure RL (GRPO) on verifiable math and code unlocks emergent reflection and aha moments, shifting scaling from pre-training compute to inference-time reasoning.

What every AI engineer needs to know about GPUs — Charles Frye, Modal

Jul 20, 2025 · 19:52

Charles Frye of Modal explains that AI engineers need to understand GPU hardware constraints to optimize inference, arguing GPUs embrace bandwidth over latency and that Tensor Cores for low-precision matrix-matrix multiplication are the key resource. He describes how GPUs achieve 16,000+ parallel threads per cycle on H100, and notes Patterson’s Law: bandwidth improves at the square of latency. The main insight: arithmetic intensity favors N² operations per N memory loads, so matrix-matrix operations are efficient while matrix-vector is wasteful. Frye demonstrates that running a small 8B model 1,000 times on the same prompt matches GPT-4 quality, and that multi-token prediction and multi-sample query become nearly free because Tensor Cores handle expanded batches as matrix-matrix multiplications. He recommends using smaller models that fit on a single GPU and scaling via multiple generations.

[Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han

Jul 19, 2025 · 2:42:28

Daniel Han of Unsloth presents a technical workshop covering reinforcement learning (RL), kernels, reasoning, quantization, and agents, arguing that RL with verifiable rewards (RLVR) is the key to unlocking LLM capabilities beyond supervised fine-tuning. He explains why open-source models plateaued after September 2024 until DeepSeek-R1 showed that RL can elicit reasoning, and breaks down PPO, GRPO, and the REINFORCE algorithm, emphasizing that GRPO removes the value model for efficiency. Han details how reward functions—not algorithms—are the hardest part, with examples like distance-based scoring for math. He demonstrates a free Colab notebook training a base model to reason, and shows that dynamic quantization can shrink models like DeepSeek-R1 from 730 GB to 140 GB with only ~1% accuracy loss, arguing that GPUs may stop getting faster after FP4 precision.

A Taxonomy for Next-gen Reasoning — Nathan Lambert, Allen Institute (AI2) & Interconnects.ai

Jul 19, 2025 · 19:21

Nathan Lambert, senior research scientist at AI2 and founder of Interconnects.ai, argues that next-generation reasoning models require a taxonomy of four traits: skills, calibration, strategy, and abstraction. While current models excel at math and code (skills), they overthink easy problems, wasting tokens and latency—calibration is needed to match output length to task difficulty. The real frontier is planning: models must learn strategy (choosing the right direction) and abstraction (breaking problems into tractable sub-tasks) to enable long-horizon agents like Deep Research and Claude Code. Lambert traces how OpenAI's Q*→Strawberry→o1 took 12–18 months of human data to teach backtracking and verification; planning should be easier because humans can write 5-step plans. He predicts post-training compute could reach parity with pre-training, citing DeepSeek's shift from 0.18% post-training compute in V3 to an estimated 10–20% for R1. The path forward: collect diverse verifiable questions, filter by difficulty, run stable RL—then scale.

OpenThoughts: Data Recipes for Reasoning Models — Ryan Marten, Bespoke Labs

Jul 19, 2025 · 19:59

Ryan Marten, co-lead of the OpenThoughts collaboration and founding engineer at Bespoke Labs, reveals the missing data recipe for open-source reasoning models, presenting OpenThoughts 3, a state-of-the-art 7B reasoning dataset that outperforms DeepSeek R1 Qwen 7B and Nematron Nano on benchmarks like AIME, Live Code Bench, and GPQA Diamond. Through over 1,000 experiments and 5,000 datasets, key findings include that sampling multiple reasoning traces per question scales performance by 16x, Qwen 32B surpasses DeepSeek R1 as a teacher model, synthetic question generation is highly effective, and filtering by difficulty or response length works better than embeddings. Surprisingly, verification of answers in SFT distillation did not improve results, and focusing on fewer high-quality sources outperformed maximizing diversity. For domain-specific reasoning, Marten advises starting with the OpenThoughts recipe, using synthetic data generation (via the open-source Curator library), and rigorous evaluation (via EvalComet). A legal reasoning example shows that distillation can surpass the teacher model. All resources are open-source.

Dream Machine: Scaling to 1m users in 4 days — Keegan McCallum, Luma AI

Jul 19, 2025 · 19:03

Keegan McCallum, Head of ML Infrastructure at Luma AI, details how the company's Dream Machine model scaled from 500 to 9,000 H100 GPUs within hours to handle 1 million users in four days, outpacing ChatGPT's initial growth. He explains that their initial Triton inference server setup was brittle and ill-suited for multi-GPU, multi-node video models, prompting a re-architecture to a custom serving stack on vanilla PyTorch. To solve work starvation across user tiers, they implemented an SLO-based aging system that ranks jobs by the percentage of their worst-case wait time elapsed. For managing dozens of model versions, they store immutable full Python environments and checkpoints in object storage, with a YAML file controlling active deployments and enabling zero-downtime rollouts across thousands of GPUs. McCallum also discusses partnerships with Nvidia, AMD, and Grok, and how Luma's broader mission is to build general multimodal intelligence that generates, understands, and operates in the physical world.

Teaching Gemini to Speak YouTube: Adapting LLMs for Video Recommendations to 2B+DAU - Devansh Tandon

Jul 16, 2025 · 22:51

Devansh Tandon, a Product Manager at Google leading YouTube's discovery system, details how YouTube adapted Gemini LLMs to power its recommendation engine for billions of daily active users. The team built SemanticID, a tokenization system that compresses video features into semantically meaningful tokens, creating a new language for YouTube content. They then continued pre-training Gemini on sequences of user watches to make the model bilingual in English and this video language. For generative retrieval, they prompt the adapted model with user demographics and watch history to output video recommendations as SemanticIDs, achieving 95%+ cost savings to serve at scale. Challenges include serving billions of users with low latency and handling video freshness—Taylor Swift's new music video must be recommendable within minutes. Tandon argues LLM-led recommendations are a bigger consumer application than search and hints at future interactive, steerable recommendations and even personalized content creation.

360Brew: LLM-based Personalized Ranking and Recommendation - Hamed and Maziar, LinkedIn AI

Jul 16, 2025 · 22:00

LinkedIn AI's Hamed Firooz and Maziar Sanjabi present BrewXL, a 150-billion-parameter foundation model for ranking and recommendation that personalizes the platform's feeds, jobs, and search. They show that training a large model then distilling to a 3B model outperforms training a small model from scratch. Scaling data, model size (from 7B to 8x22B), and context length all improve performance, though context beyond the trained length degrades accuracy. The model achieves zero-shot generalization on out-of-domain tasks, matching or beating task-specific production models, and significantly reduces the cold-start gap for users with fewer than five interactions. For serving, they combine gradual pruning, mixed-precision quantization (FP8 for most layers, FP32 for the LM head), and 4D attention masks to score up to 500 items without cross-attention, yielding a 7X latency reduction and 30X throughput increase per GPU.

What We Learned from Using LLMs in Pinterest — Mukuntha Narayanan, Han Wang, Pinterest

Jul 16, 2025 · 18:13

Pinterest search engineers Han Wang and Mukuntha Narayanan present four key learnings from integrating LLMs into the platform's search relevance system. Fine-tuned LLM cross-encoders improve relevance prediction by 12% over multilingual BERT and 20% over SearchSage using an 8B model. VLM-generated image captions and user action features enrich pin text representations and further boost performance. Knowledge distillation into a bi-encoder student model, trained on 100x more data via semi-supervised learning, enables production serving with real-time query embeddings and 85% cache hit rate. Relevance-tuned embeddings serve as general-purpose semantic representations across multiple surfaces, and the system yields relevance gains in multiple languages and countries despite a predominantly US training set.

RL for Autonomous Coding — Aakanksha Chowdhery, Reflection.ai

Jul 16, 2025 · 19:27

Aakanksha Chowdhery, CEO of Reflection AI and former lead researcher on PaLM and Gemini at Google, argues that reinforcement learning (RL) scaling is the next frontier for autonomous coding agents. She explains that inference-time techniques like majority voting and self-revision improve accuracy but require many samples (e.g., 10,000 for rare correct generations). RL training can learn to generate correct outputs directly, especially in verifiable domains like code with unit tests. Reflection AI aims to build superintelligence starting with autonomous coding, leveraging automated verification to design better reward functions. She notes challenges in scaling RL, including system complexity and reward hacking, but sees coding as ideal due to execution feedback.

Thinking Deeper in Gemini — Jack Rae, Google DeepMind

Jul 10, 2025 · 18:13

Jack Rae, lead of Gemini Thinking at Google DeepMind, presents thinking as a solution to the fixed test-time compute bottleneck in large language models. He explains that Gemini inserts a thinking stage where the model iterates via reinforcement learning, learning to self-correct and explore multiple strategies. This enables a continuous cost-performance tradeoff via thinking budgets, improving reasoning on math and code. Future work includes Deep Think, which raises USA Math Olympiad performance from the 50th to the 65th percentile by scaling inference compute further. Rae envisions models that, like mathematician Ramanujan, achieve deep, data-efficient reasoning from limited knowledge.

A year of Gemini progress + what comes next — Logan Kilpatrick, Google DeepMind

Jul 10, 2025 · 11:58

Logan Kilpatrick, head of product for Google AI Studio at DeepMind, announces the final update to Gemini 2.5 Pro, which achieves state-of-the-art results on Aider and HLE benchmarks. He details Google's 50x increase in AI inference over the past year, driven by merging research and product teams into DeepMind. The episode outlines Gemini's evolution toward a universal assistant that unifies Google products, with upcoming features including proactivity, native audio and video capabilities (Veo), and smaller models. Kilpatrick also previews developer-focused updates: a SOTA embeddings model, a deep research API, and Veo 3 and Imagine 4 in the API, alongside repositioning AI Studio as a dedicated developer platform.

2025 in LLMs so far, illustrated by Pelicans on Bicycles — Simon Willison

Jul 9, 2025 · 18:30

Simon Willison reviews the past six months of LLM releases — including AWS Nova, Llama 3.3 70B, DeepSeek R1, Mistral Small 3, Claude 3.7 Sonnet, GPT 4.5, Gemini 2.5 Pro, GPT-4o, Llama 4, GPT 4.1, O3/O4 Mini, and Claude 4 — using his 'pelican on bicycle' SVG benchmark to argue that local models have become good enough to run GPT-4 class models on a laptop and that combining tools with reasoning is the most powerful technique in AI engineering, while noting risks like prompt injection and the 'lethal trifecta'. He tracks 30 significant model releases, highlighting that Mistral Small 3 (24B) matches Llama 3 70B's performance, which itself matched the 405B model, enabling local inference. DeepSeek's R1 caused a $500B+ Nvidia stock drop on January 27. GPT 4.1 Nano is the cheapest model yet at a fraction of a cent per pelican. He also examines bugs: ChatGPT's sycophantic 'shit-on-a-stick' incident and Claude 4's tendency to snitch to authorities when given ethical instructions and email tools. Willison concludes that while the pace is accelerating, control over context and security remain critical.

Trends Across the AI Frontier — George Cameron, ArtificialAnalysis.ai

Jul 8, 2025 · 17:52

George Cameron of Artificial Analysis presents multiple frontiers—reasoning, open-weights, cost, speed—across the AI stack, arguing that trade-offs between intelligence, latency, and expense are critical for building applications. Reasoning models like O4 mini high use an order of magnitude more output tokens (72M vs GPT-4.1's 7M) and take over 40 seconds per response versus 4.7 seconds, impacting agentic workflows where 30 sequential calls multiply latency. The open-weights gap has nearly closed, with China-based labs like DeepSeek R1 and Alibaba's Qwen 3 leading. O3 cost roughly $2,000 to run the intelligence index, while GPT-4.1 nano is over 500 times cheaper. Output speeds have jumped from GPT-4's 40 tokens/s in 2023 to over 1,000 on a B200 accelerator. Despite efficiency gains, demand for compute will keep rising due to larger models, reasoning’s extra tokens, and multi-step agents.

New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games

Jul 5, 2025 · 18:31

Shafik Quoraishee, a game developer at NYT Games, presents his independent research into applying AI to solve the New York Times Connections word game. He explains that Connections, launched in June 2023 and second only to Wordle with hundreds of millions of plays, challenges AI's abstract reasoning through intentional decoys and overlapping categories. Quoraishee models the game as a graph coloring problem and uses semantic similarity, relational alignment scores across multiple dimensions (orthography, morphology, encyclopedic, etc.), and graph neural networks combined with reinforcement learning to build a solver. His preliminary results show increased solvability for hard puzzles, contrasting with LLMs that may simply recall internet solutions. The work aims to provide a transparent, explainable AI approach for puzzle-solving and potential game development applications.

Optimizing inference for voice models in production - Philip Kiely, Baseten

Jul 1, 2025 · 15:13

Philip Kiely of Baseten shows how open-source TTS models like Orpheus TTS, built on a LLaMA 3.2 3B backbone, can be optimized for production inference using LLM tooling such as TensorRT-LLM and FP8 quantization, achieving time-to-first-byte (TTFB) under 150 milliseconds and supporting 16–24 simultaneous streams on a single half-H100 GPU. He argues that because TTS models are architecturally similar to LLMs, techniques like dynamic batching, KV-cache quantization, and Torch Compile on the audio decoder can dramatically reduce latency and increase concurrency. He emphasizes that for real-time voice agents, the goal is not raw tokens per second (real-time requires only 83 TPS for Orpheus) but low TTFB and high throughput to minimize GPU spend. However, Kiely warns that non-runtime factors—such as client code using sequential requests without session reuse or sending traffic to distant data centers—can easily add back the milliseconds saved at the model level, and that infrastructure connecting listening, thinking, and talking pipelines is often the dominant source of latency.

Serving Voice AI at Scale — Arjun Desai (Cartesia) & Rohit Talluri (AWS)

Jun 27, 2025 · 17:05

Arjun Desai of Cartesia AI and AWS's Rohit Talluri discuss scaling voice AI for enterprise, arguing that latency and controllability are critical, with Cartesia's state-space model Sonic 2 achieving 40ms model latency for real-time applications. Desai explains that traditional transformer models scale quadratically, while Cartesia's SSMs maintain O(1) generation, enabling 2.5x faster inference than their earlier models. He emphasizes that edge deployment is 5x faster than cloud round-trips, making local models essential for interactive use cases. On quality, Desai notes that voice AI must handle interruptions, accents, and background noise, and that Cartesia's voice marketplace amplifies human voice actors rather than replacing them. Looking to 2030, he predicts voice AI will become the default interface across healthcare, customer support, and gaming, with interactive models extending beyond audio to full world models.

How fast are LLM inference engines anyway? — Charles Frye, Modal

Jun 27, 2025 · 16:07

Charles Frye presents benchmarks from hundreds of runs on Modal comparing open-source inference engines VLM, SGLang, and TensorRTLM across models like Qwen 3 and Gemma 27B, arguing open weights models have caught up to proprietary ones, making self-hosting viable. He shows, for example, that Qwen 3 (MoE) on VLM achieves ~1 request/sec with 128 input tokens and 1024 output tokens, while switching to a RAG-like workload (1024 in, 128 out) yields a 4x throughput improvement. Frye warns that optimizing for context over reasoning can improve latency without sacrificing quality, and notes that the engines' out-of-the-box performance varies by model—e.g., SGLang underperforms VLM on Gemma due to less optimization. He also highlights the gap between prefill (parallel) and decode (autoregressive) speeds, which a rationalist would expect from transformer architecture. The benchmarks, available at modal.com/llmalmanac, aim to help engineers choose hardware and engines, with contributions welcome for optimized configs like TensorRTLM's knobs.

Fun stories from building OpenRouter and where all this is going - Alex Atallah, OpenRouter

Jun 25, 2025 · 18:47

Alex Atallah, founder of OpenRouter, tells the story of how he launched the LLM aggregator in early 2023 after observing the open-source race sparked by Meta's Llama 1 and Stanford's Alpaca distillation under $600. He argues the inference market is not winner-take-all, citing OpenRouter's data showing Google Gemini growing from 2-3% to 34-35% of tokens over 12 months. The platform evolved from a simple model collection into a marketplace with over 400 models and 60 providers, solving architecture challenges like 30-millisecond latency, cancelable streams, and a middleware plugin system for web search and PDF parsing. Atallah explains that OpenRouter grew 10-100% month-over-month for two years, and he predicts future additions like transfusion models that generate images and more powerful geographic routing for enterprise optimization.

Luminal - Search-Based Deep Learning Compilers - Joe Fioti

Jun 3, 2025 · 24:35

Joe Fioti presents Luminal, a search-based deep learning compiler that simplifies ML libraries to 12 primitive operations and uses search to automatically discover optimized kernels like flash attention. By representing models as directed acyclic graphs of these simple ops, Luminal keeps its codebase under 5,000 lines yet can run all major models. Its compiler applies 20-25 rewrite rules to search through equivalent GPU kernels, profiling to find the fastest—automatically rediscovering flash attention, an algorithm that took five years for the industry to develop. Data movement accounts for 99% of runtime, so kernel fusion merges many ops into one, dramatically speeding execution. An external auto-grad crate adds training support without altering the core. Future plans include supporting AMD, TPUs, and a serverless cloud that exports optimized graphs for inference.

Text-to-Speech Data Preparation and Fine-tuning Workshop - Ronan McGovern

Jun 3, 2025 · 34:00

Ronan McGovern walks through fine-tuning Sesame's CSM-1B text-to-speech model on a specific voice, using a YouTube video as the data source. He explains token-based TTS models, including how audio is represented via codebooks and how CSM-1B uses a main transformer for zeroth tokens and a secondary transformer for 31 hierarchical tokens. The workshop covers data preparation: downloading audio with yt-dlp, transcribing with Whisper Turbo, manually correcting the transcript, and splitting audio into 30-second chunks (41 clips from a 30-minute video). Fine-tuning uses Unsloth with LoRA adapters (rank 32, alpha 16) on linear layers, training for one epoch with a batch size of 2 and virtual size of 8, reducing loss from ~6.34 to ~3.72. Evaluation compares zero-shot inference (random speaker), voice cloning (closer but imperfect), and fine-tuned plus cloning (best result, producing an Irish-accented voice with natural errors). McGovern recommends 50+ 30-second clips for noticeable effect and notes that combining fine-tuning with voice cloning yields good performance even with limited data.

Finetuning: 500m AI agents in production with 2 engineers — Mustafa Ali & Kyle Corbitt

Apr 12, 2025 · 18:44

Method Financial and OpenPipe detail how Method scaled AI financial agents to 500 million daily interactions using fine-tuned open-source models instead of expensive GPT-4. After racking up $70,000 in monthly GPT-4 costs and facing latency and error issues, they fine-tuned an 8B parameter LLaMA 3.1 model to achieve under 200ms latency and 9% error rate (beating GPT-4o's 11% at lower cost). Mustafa Ali explains that the key was using production data from GPT as training data and choosing the cheapest model that met performance goals. Kyle Corbitt emphasizes that fine-tuning is a power tool for bending the price-performance curve when prompt engineering falls short. The episode argues that productionizing AI agents requires patience and openness from engineering teams, and concludes with a call for software engineers to pivot to AI engineering.

Trust, but Verify: Knowledge Agents for Finance Workflows - Mike Conover

Apr 9, 2025 · 21:10

Mike Conover, CEO of Brightwave, explains how his company builds knowledge agents for financial workflows, digesting thousands of pages of content for due diligence and research. He argues that non-reasoning models perform only local search, so winning systems must use end-to-end reinforcement learning over tool-use calls to achieve globally optimal outputs. Conover describes design patterns like decomposing research into sub-themes, using secondary calls for error correction, and avoiding the latency trap of long feedback loops. He emphasizes that synthesis—weaving facts across documents—remains a hard problem due to limited recombinative reasoning in training data. Brightwave's product reveals its thought process through interactive citations and structured findings, allowing analysts to drill into any passage. The episode also notes the company is hiring, offering a $10,000 referral bonus.

Customized, production ready inference with open source models: Dmytro (Dima) Dzhulgakov

Feb 16, 2025 · 18:55

Dmytro (Dima) Dzhulgakov, co-founder and CTO of Fireworks AI, argues that open source models are the future for production Gen AI applications, and Fireworks provides a platform to make them customized and production-ready. He explains that while proprietary models like GPT-4 are powerful, they are too large, expensive, and slow for many use cases, whereas open models like Llama or Gemma can be fine-tuned for specific domains to achieve better quality at up to 10x speed and lower cost. The main challenges of using open models—complex setup, performance tuning, and production scaling—are addressed by Fireworks' custom serving stack, which achieves the fastest long-prompt inference and serves SDXL fastest among providers, handling over 150 billion tokens per day. The platform supports fine-tuning and serving thousands of LoRA adapters on the same GPU with serverless pay-per-token pricing. Dzhulgakov also highlights the emerging architecture of compound AI systems, where function calling—exemplified by the open-source Fire Function model—connects LLMs to external tools and knowledge sources, enabling agentic applications like stock querying and chart generation. The episode…

Training Albatross An Expert Finance LLM: Leo Pekelis

Feb 13, 2025 · 16:20

Leo Pekelis, chief scientist at Gradient, explains how they transformed an open-source model into Albatross, a finance LLM that tops leaderboards on both general and domain-specific tasks. The key was an automated data pipeline using membership inference to curate finance data from a massive corpus, followed by continual pre-training and alignment via supervised fine-tuning and preference optimization. He also details a one-million-token context extension on a Llama 3-based model that achieves 100% needle-in-the-haystack scores, enabling in-context learning with thousands of examples to reduce hallucinations. The models, v-alpha-tross and the extended-context Llama 3, are open-sourced on Hugging Face.

Accelerating Mixture of Experts Training With Rail Optimized InfiniBand Networking in Crusoe Cloud

Feb 12, 2025 · 17:45

Ievgen Bakulenko, product manager at Crusoe Cloud, explains how their rail-optimized InfiniBand networking accelerates training for sparse mixture of experts models. By leveraging NVIDIA's PXN feature, which allows GPUs to communicate across different rails using the internal NVSwitch in a single hop, Crusoe achieves a 50% improvement in synthetic benchmark latency and bandwidth for both small and large messages. In a real-world test fine-tuning the Mixtral model (8 feed-forward blocks, 7 billion parameters) on 240 H100 GPUs, this topology reduced training time by 14%, directly lowering cost and time-to-train. Bakulenko also outlines Crusoe's AI cloud platform, its climate-aligned mission using stranded energy, and its focus on easy-to-use infrastructure for AI engineers.

System Design for Next-Gen Frontier Models — Dylan Patel, SemiAnalysis

Feb 11, 2025 · 18:29

Dylan Patel of SemiAnalysis breaks down the inference challenges for next-generation frontier models like GPT-4 (1.8 trillion parameters) and upcoming models trained on 100,000+ GPU clusters. He emphasizes that prefill (prompt processing) is compute-intensive while decode (token generation) is memory bandwidth-intensive, creating a systems problem where serving 64 users at 30 tokens/second requires 60 terabytes/second of memory bandwidth. Patel details engineering strategies such as continuous batching to improve batch utilization by 10-100x, disaggregated prefill to isolate noisy neighbors and maintain time-to-first-token SLAs, and context caching (like Google's) to cache KV cache on CPU/storage instead of GPU memory, dramatically reducing prefill costs. He warns that open-source tools like LLaMA.cpp lack these optimizations, making high-performance serving of models like LLaMA 405b infeasible without libraries like vLLM or TensorRT-LLM. On scaling, Patel notes that 100,000 GPU clusters (e.g., Microsoft's Arizona data center consuming 150 MW) face reliability issues — optical transceivers fail every five minutes — and straggler chips (silicon lottery) can degrade training…

Fine tune 20 Llama Models in 5 Minutes: Santosh Radha

Feb 9, 2025 · 6:26

Santosh Radha, Head of Product/Research at Agnostiq, demonstrates Covalent, an open-source platform that lets users fine-tune and deploy hundreds of Llama models directly from Python without Kubernetes or Docker. By adding a single decorator to Python functions, users specify GPU requirements (e.g., H100 with 48 GB, 18-hour limit) and run them on remote compute, paying only for actual usage (e.g., 87 cents for 6 minutes on an L14, 11 cents on a V100). Covalent supports job submission, inference endpoints with custom autoscaling (e.g., scale to 10 GPUs at 9 AM daily), and automated workflows for training, evaluation, and deployment. Radha shows a workflow that iterates over 20 models, fine-tunes each, evaluates accuracy, sorts, and deploys the best—all from a Jupyter notebook with a single dispatch call. The talk, recorded at the AI Engineer World's Fair, emphasizes eliminating infrastructure overhead for accelerated compute.

The GenAI Maturity Curve or You Probably Don't Need Fine Tuning: Kyle Corbitt

Feb 9, 2025 · 18:03

Kyle Corbitt, CEO of OpenPipe, argues that most teams don't yet need fine-tuning and should start with prompted models like GPT-4. He presents a GenAI maturity curve where the trigger to fine-tune is when you hit constraints on cost, latency, or quality consistency—for example, if GPT-4 is 80-90% correct but inconsistent on the last 10-20%. Fine-tuning shifts the paradigm frontier outward, enabling models like fine-tuned LLaMA 38B to outperform GPT-4 at 1/25th the cost. The process has four steps: capture production logs to know your input distribution, prepare high-quality data (using GPT-4 outputs or iterative labeling), train with one-click tools, and evaluate with inner-loop (LLM-as-judge) and outer-loop (business metrics) evals. OpenPipe and other providers make deployment trivial via OpenAI-compatible APIs. The talk delivers a concrete decision framework and a walkthrough so any engineer can fine-tune in under an hour.

LLM Quality Optimization Bootcamp: Thierry Moreau and Pedro Torruella

Feb 8, 2025 · 53:05

Thierry Moreau of OctoAI demonstrates how to fine-tune Llama 3 8B on a PII redaction task using OpenPipe and OctoAI, achieving 47% better accuracy and a 200x cost reduction (from $30 to $0.15 per million tokens) compared to GPT-4 Turbo. He explains that fine-tuning should follow prompt engineering and RAG, and works best for specialized tasks like function calling. The talk walks through building a fine-tuning dataset from the PI Masking 200k dataset, using OpenPipe to train a LoRA for $40, deploying it on OctoAI, and evaluating it to show the fine-tuned model scores 0.97 accuracy versus GPT-4’s 0.68. Moreau emphasizes that this continuous deployment cycle requires monitoring data drift and retraining, but tools like OpenPipe and OctoAI make it accessible even for teams without deep ML expertise.

Insights from Snorkel AI running Azure AI Infrastructure: Humza Iqbal and Lachlan Ainley

Feb 8, 2025 · 20:46

Humza Iqbal of Snorkel AI explains how the company uses Azure AI infrastructure powered by NVIDIA GPUs to fine-tune foundation models for enterprise customers, achieving better performance per dollar by switching from A100s to H100s. He details their distributed training stack (PyTorch, Horovod, NFS) and lessons learned such as balancing node count for batch size and monitoring GPU utilization to avoid networking or data-loading bottlenecks. A cost comparison found two H100s outperformed four A100s on both training and inference, enabling faster iteration through more synthetic data. Azure's dedicated VMs, reliable NFS throughput, and flexible capacity allowed Snorkel to scale experiments from single-node to dozens of GPUs. Future work includes programmatic preference signals and multimodal retrieval algorithms, all planned on Azure.

No-code fine-tuning: Mark Hennings

Feb 5, 2025 · 9:27

Mark Hennings, creator of Entrypoint, argues that fine-tuning large language models is now accessible without code, offering faster and cheaper alternatives to prompt engineering: GPT-3.5 fine-tuned runs at 73ms per token vs GPT-4's 196ms, saving 88.6% in cost and cutting prompts by 90%. He explains that fine-tuning reduces prompt injection risks, enables team collaboration via training data, and only requires 20 examples to start. Hennings demonstrates Entrypoint's no-code UI that lets users import CSV data, structure fields with templating, and fine-tune GPT-3.5 Turbo, then iteratively improve models by feeding production feedback back into the dataset. He proposes a dev lifecycle: prototype with prompt engineering, use it to build a dataset, fine-tune, evaluate, deploy, and continuously refine.

Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou

Jan 1, 2025 · 33:39

NVIDIA solutions architect Mark Moyou explains that LLM inference differs fundamentally from standard deep learning deployment, requiring careful management of KV Cache, attention mechanisms, and GPU memory to control cost. He details how tokens are processed: prefill computes attention across the entire prompt, then generation produces one token at a time, with KV Cache storing key-value pairs to avoid recomputation. Llama's 32 attention heads and FP8 quantization (halving memory with near-identical accuracy) are cited as key optimizations. Moyou emphasizes measuring time to first token, inter-token latency, and input/output sequence length distributions to size inference engines. He presents NVIDIA's TRT-LLM (model compilation for LLMs) and Triton inference server as tools to maximize throughput, and discusses how query patterns like long-input-short-output or short-input-long-output impact GPU utilization and deployment cost.

AI Frontiers in Trust and Safety Combatting Multifaceted Harm on Tinder at Scale: Vibhor Kumar

Dec 2, 2024 · 14:36

Vibhor Kumar, senior AI engineer at Tinder, explains how the company uses open-source LLMs and LoRAX to detect a long tail of trust and safety violations at global scale. Facing challenges like content pollution and automated fraud from generative AI, Tinder leverages pre-trained models such as LLaMA and Mistral, fine-tuning them with LoRA and QLoRA on hybrid datasets generated by GPT-4 and manually verified. They serve dozens of fine-tuned adapters on a single GPU using LoRAX, achieving real-time inference (tens of QPS, ~100ms latency) for categories including hate speech, pig butchering scams, and underage users. The approach yields near 100% recall on simpler tasks and significant improvements over baselines, with better generalization that resists adversarial evasion. Future directions include visual language models for explicit image detection and automating retraining pipelines.

Decoding Mistral AI's Large Language Models: Devendra Chaplot

Nov 21, 2024 · 18:16

Devendra Singh Chaplot of Mistral AI details the company's open-source large language models, including Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, and CodeStral 22B, arguing that open models complement rather than compete with profit by serving as branding tools and driving customer acquisition for proprietary upgrades. He explains the three-stage LLM training process—pre-training on trillions of tokens, instruction tuning with prompt-response pairs, and learning from human feedback via preference optimization—emphasizing that more data does not guarantee better performance due to noise. The episode highlights Mistral's focus on optimizing the performance-to-cost ratio, with CodeStral 22B outperforming larger models like Code LLaMA 70B while being smaller and multilingual across 80+ programming languages. Practical guidance is given: prototype with high-end commercial models, then fine-tune open models for specific tasks to balance performance and cost.

A Practical Guide to Efficient AI: Shelby Heinecke

Nov 18, 2024 · 17:45

Shelby Heinecke, who leads an AI research team at Salesforce, presents five orthogonal dimensions for making AI models efficient: efficient architecture selection, pre-training, fine-tuning, inference, and prompting. She highlights the power of small models like Phi-3 (3.8B parameters outperforming a 7B model), mobile LLM (350M parameters on par with 7B after fine-tuning), and Octopus (2B fine-tuned Gemma exceeding GPT-4 on Android tasks). For efficient inference, she explains post-training quantization, showing 4-bit quantization nearly halves memory usage without performance loss (e.g., LLaMA models), but warns 3-bit can degrade quality. She recommends frameworks like LLaMA CBP and ONNX Runtime for quantization and introduces her team's open-source Mobile AI Bench for evaluating quantized models, including an iOS app to measure latency and battery drain. The central claim is that deploying AI in constrained environments—cloud, on-prem, or edge—demands efficiency, and these practical techniques bridge the gap from demo to production.

What It Actually Takes to Deploy GenAI Applications to Enterprises: Arjun Bansal and Trey Doig

Nov 4, 2024 · 21:30

Trey Doig of Echo AI and Arjun Bansal of Log10 recount Echo AI's journey deploying a GenAI-native conversational intelligence platform for billion-dollar retail brands, focusing on the centrality of accuracy. Echo AI ingests all customer conversations, uses LLMs to surface insights at 100% coverage, but must overcome enterprise trust issues by achieving 95% accuracy within seven days. The platform relies on Log10's auto feedback system, which uses AI-based review to match human accuracy with model speed, yielding a 20 F1 point improvement in one use case. The episode details how Echo AI's solution engineers use Log10 to grade summarizations, catch hallucinations, and track model drift, turning human feedback into curated datasets for fine-tuning. Ultimately, the partnership demonstrates a path to self-improving LLM applications through iterative accuracy measurement and improvement.

State Space Models for Realtime Multimodal Intelligence: Karan Goel

Oct 29, 2024 · 14:26

Karan Goel, founder of Cartesia, argues that state space models (SSMs) are key to real-time multimodal intelligence, offering cheaper, faster, and higher-quality alternatives to transformers for streaming applications like conversational voice and on-device assistants. He contrasts batch intelligence (cloud APIs) with streaming intelligence needed for low-latency tasks, emphasizing that SSMs compress information linearly rather than storing all tokens, enabling efficient long-context processing. Cartesia’s voice generation model achieves instant latency in the data center and is being optimized for on-device Mac and desktop deployment. Goel asserts that compression helps long-context tasks (e.g., 24-hour security footage analysis) more than retrieval, and that SSMs now match transformer quality while scaling better for multimodal data.

Productionizing GenAI Models – Lessons from the world's best AI teams: Lukas Biewald

Oct 23, 2024 · 22:36

Lukas Biewald, founder of Weights and Biases, shares lessons from productionizing GenAI models, emphasizing that while AI is easy to demo, it is hard to productionize. He illustrates this with a personal project building a custom Alexa-like device using LLaMA and Whisper, where accuracy improved from 0% to 98% through prompt engineering, switching to Mistral, and fine-tuning with QLoRA. Biewald argues that tracking experiments (including failures) is critical for reproducibility and collaboration, and that a robust evaluation framework—beyond 'testing by vibes'—is essential for iterating and shipping v2. He notes that 70% of the audience had LLM apps in production, yet many lack solid evaluations, and recommends starting with lightweight prototypes and incorporating end-user feedback.

The Hierarchy of Needs for Training Dataset Development: Chang She and Noah Shpak

Oct 15, 2024 · 16:32

Chang She (CEO of LanceDB) and Noah Shpak (AI data platform lead at Character AI) argue that data infrastructure is the critical bottleneck for LLM training, and LanceDB's columnar format solves the 'new cap theorem' for AI: needing fast scans, random access, and handling large multimodal blobs simultaneously. Noah explains how Character AI structures pre-training around wide domain coverage and post-training around granular analytics like token counts and difficulty scores, using synthetic data, quality scoring, and dataset selection to improve models. Chang details how Lance format provides zero-copy schema evolution, time travel, and indexing extensions for vector, scalar, and full-text search, enabling a single table to serve SQL analytics, PyTorch training, and production vector search. The episode emphasizes that speed and iterative dataset management are key to accelerating AI research, with LanceDB facilitating cheap random access and low-infra billion-scale vector search.

Making Open Models 10x faster and better for Modern Application Innovation: Dmytro (Dima) Dzhulgakov

Oct 9, 2024 · 18:55

Dmytro Dzhulgakov, CTO of Fireworks AI, argues that open-source models are the future for GenAI applications because they offer lower latency, lower cost, and domain adaptability compared to proprietary models. He explains that open models can be up to 10x faster for narrow domains and cut costs significantly, using examples like fine-tuned Llama 3 for function calling. Fireworks addresses the challenges of setup, optimization, and production readiness with a custom serving stack that delivers the fastest inference for long prompts and image generation (e.g., SDXL). He highlights FireFunction V2, an open-source model for function calling that combines chat and tool use, and notes that Fireworks serves over 150 billion tokens per day for companies like Quora and Cursor. The talk emphasizes that platforms like Fireworks enable developers to start with serverless inference, fine-tune models, and scale to enterprise-grade deployment with dedicated hardware.

Everything you need to know about Fine-tuning and Merging LLMs: Maxime Labonne

Sep 25, 2024 · 17:52

Maxime Labonne from Liquid AI explains the LLM training lifecycle—pre-training, supervised fine-tuning (SFT), and preference alignment—and when to use fine-tuning over prompt engineering. He details SFT dataset creation (accuracy, diversity, complexity) and techniques like full fine-tuning, LoRA, and QLoRA, with key hyperparameters. The core of the talk is model merging: combining weights of fine-tuned models without GPU, using methods like SLERP (spherical linear interpolation for two models), TIES (pruning redundant parameters to merge many models), pass-through (concatenating layers, e.g., Meta LLaMA 3 120B Instruct by repeating layers gives strong creative writing), and Franken-MoE (extracting FFN layers from domain-specific models with a router). Labonne demonstrates these with his NeuralBeagle and Beyonder models, noting merged models dominate the OpenLLM leaderboard and that TIES merging often outperforms more experimental Mixture of Experts approaches.

From model weights to API endpoint with TensorRT LLM: Philip Kiely and Pankaj Gupta

Sep 13, 2024 · 1:40:01

Philip Kiely and Pankaj Gupta of Baseten lead a workshop on TensorRT-LLM, NVIDIA's high-performance inference framework for LLMs, arguing its use delivers best-in-class throughput and latency on NVIDIA GPUs. They explain that TensorRT-LLM optimizes computational graphs via plugin kernels and in-flight batching, achieving 216 tokens per second and 180ms time to first token on Mistral 7B. The workshop demonstrates building an engine for TinyLlama 1.1B, including FP8 quantization that reduced engine size from 2 GB to 1.2 GB with minimal quality loss. They benchmark a deployed model, showing 7,000 total tokens per second at batch size 64 on an A10G. The presenters compare TensorRT-LLM favorably to VLLM for high-throughput production use and introduce Truss, Baseten's open-source packaging tool, alongside their managed platform for automatic scaling and fast cold starts.

Low Level Technicals of LLMs: Daniel Han

Jul 31, 2024 · 2:52:26

Daniel Han of Unsloth explains how to find and fix bugs in open-source LLMs like Gemma, Phi-3, and Llama, covering tokenizer issues, architecture pitfalls, and finetuning optimizations. He details the eight Gemma bugs Unsloth fixed, including a critical RoPE downcasting error that broke positional encoding, and a 2048 sliding window bug in Phi-3. Han walks through transformer internals: attention masking, layer norms, RoPE embeddings, and SwiGLU activation, showing how to derive gradients for custom kernels. He demonstrates Unsloth's 2x faster finetuning with 70% less memory via Triton kernels, and introduces new features: automatic Ollama model file creation, CSV fine-tuning with merged columns, and chunked cross-entropy for large vocabularies. The session includes live Q&A on learning rate schedules, precision trade-offs, and mechanistic interpretability.

Fixing bugs in Gemma, Llama, & Phi 3: Daniel Han

Jul 31, 2024 · 17:42

Daniel Han of Unsloth details eight bugs found in Llama 3, including double BOS tokens, untrained tokens in the base model, and pad tokens equaling EOS tokens, which cause infinite generations. He explains how Unsloth automatically fixes these issues and offers a free Colab notebook for fine-tuning with Ollama. Han also covers tokenization fixes for Gemma and a sliding window bug in Phi-3, emphasizing the importance of correct chat templates and avoiding double BOS tokens. The episode provides concrete code examples and best practices for fine-tuning open-source LLMs to avoid common pitfalls.

Unlocking Developer Productivity across CPU and GPU with MAX: Chris Lattner

Jul 25, 2024 · 18:33

Chris Lattner, CEO of Modular, presents MAX, a unified AI framework that accelerates Gen AI inference by combining CPU and GPU programming into a single Pythonic model, and Mojo, a new programming language that extends Python to systems programming with 100–1000x speedups. Lattner argues that current fragmentation across PyTorch, ONNX, TensorRT, and hardware-specific libraries slows innovation, and MAX replaces the entire stack—including cuDNN and Intel MKL—with a consistent, compiler-driven approach. He demonstrates that MAX's Int4/Int6 quantization achieves 5x faster performance than llama.cpp on cloud CPUs, and that its GPU matrix multiplication beats NVIDIA's cuBLAS by up to 30%. Mojo enables developers to write for loops and tokenizers (e.g., for LLaMA 3) in Python-like syntax without dropping to C++ or Rust. MAX is free and available now for CPU inference; GPU support launches in September with early access via Discord.

Llamafile: bringing AI to the masses with fast CPU inference: Stephen Hood and Justine Tunney

Jul 16, 2024 · 17:25

Stephen Hood and Justine Tunney present Mozilla's Llamafile project, which turns AI model weights into single-file executables that run on any OS and CPU without installation, democratizing access to AI. They claim CPU inference can match GPU performance through techniques like outer-loop unrolling in matrix multiplication and using a GPU-like programming model with sync threads, achieving 30-500% speed increases. Justine demonstrates a summarization task where the optimized version completes in seconds versus the old version's many seconds. Hood announces the Mozilla Builders accelerator offering $100,000 in non-dilutive funding for open-source local AI projects, emphasizing that individuals and small groups can still make impactful contributions in AI.

Harnessing the Power of LLMs Locally: Mithun Hunsur

Nov 22, 2023 · 17:09

Mithun Hunsur presents llm.rs, a Rust library for running large language models locally, arguing it gives developers ownership, lower latency, and privacy compared to cloud APIs. He explains how quantization makes inference viable on consumer hardware, and shows that llm.rs supports architectures like LLaMA and Falcon through a unified interface. Practical code examples demonstrate customization, and community projects like LocalAI and LLMchain illustrate real-world use. Hunsur shares his own date-extraction pipeline, fine-tuning a small model with GPT-3 data to replace expensive cloud calls. He also cautions about hardware requirements, trade-offs between speed and quality, and ecosystem churn from rapid innovation.

Domain adaptation and fine-tuning for domain-specific LLMs: Abi Aryan

Nov 14, 2023 · 25:09

Abi Aryan's talk covers domain adaptation and fine-tuning for large language models, contrasting prompting, RAGs, and three fine-tuning methods: adaptive, behavioral, and parameter-efficient. Adaptive fine-tuning adds small adapter modules (0.15% of parameters) for new domains like biochemical engineering; behavioral fine-tuning optimizes label space for a single task; and parameter-efficient methods like LoRA and QLoRA reduce model size via low-rank adaptation and four-bit precision, ideal for low-resource devices. Aryan emphasizes data quality—deduplication reduces memorization—and practical tips: batch size of 32 or 64, starting with 100 epochs, using Adam optimizer, gradient checkpointing for memory savings, and in-context learning with dynamic examples to handle drift. Evaluation should combine metric-based (Bleu, Rouge), tool-based (Weights & Biases), model-based, and human-in-the-loop approaches, though full pipeline considerations (data collection, base model choice, storage) are critical for robust applications.

[Workshop] AI Engineering 201: Inference

Nov 7, 2023 · 1:43:16

Charles Frye, instructor of the Full Stack LLM Bootcamp, leads a workshop on AI engineering inference, focusing on the build-versus-buy decision between proprietary and open models. He argues that proprietary models like OpenAI's GPT-4 and Anthropic's Claude are currently more capable but expensive, while open models like LLaMA 2 are less capable but offer hackability, though they may catch up if capabilities requirements saturate. Frye covers inference on end-user devices, noting that running models locally avoids network latency but faces tight memory and power constraints—e.g., a 7B parameter model requires 14 GB, too large for phone RAM. He explains inference-as-a-service (e.g., OpenAI, Replicate) versus self-serving on cloud GPUs or serverless platforms like Modal, highlighting that memory bandwidth is the bottleneck: GPUs have 1.5 TB/s memory bandwidth vs 312 TFLOPS compute, so batching is crucial for throughput. Frye also discusses inference arithmetic, custom silicon like TPUs which offer ~30% better efficiency but not drastic gains, and containerization challenges for GPU workloads.