Nematron

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.

Morgan Stanley's ALPHALAB: Multi-Agent Research Across Optimization Domains — Brendan Rappazzo

Jul 29, 2026 · 20:07

Morgan Stanley's Brendan Rappazzo presents AlphaLab, an open-source multi-agent system that automates quant research by having agents write code, set up backtests, and run experiments, arguing that the lasting human role is designing verifiable environments like a private Kaggle. The system uses a strategist agent that proposes experiments and worker agents that implement them, managed via a Kanban board, and skipped off-the-shelf frameworks to maintain control. Rappazzo reports real improvements found internally, including a top 12% finish in a Kaggle competition fine-tuning Nvidia's Nematron model. He emphasizes that the key is building good evals and environments, which encode enterprise expertise, and that the ultimate goal is a self-improving system where the auto research optimizes itself.