3bit 27B Model Runs at Usable Speed for Reinforcement Learning
cephaloform · x · 2026-08-01
A developer shared an exciting breakthrough in local deployment: successfully running a 3bit quantized 27B model at a usable speed within a Reinforcement Learning (RL) workflow. This indicates that extreme quantization techniques are making heavy models, which typically require massive compute, smoothly viable for everyday development and agentic tasks.
Related event: 3bit Quantized 27B Model Achieves Usable Speed in RL Workflows(2 posts)→
More from Infra
- DeepSeek-V3 Trained With Only 180K GPU-Hours, Slashing MoE Compute Costs — teortaxesTex · 2026-08-01
- Tata in Talks with ASML to Manufacture Advanced Chipmaking Subassemblies in India — prasanna_says · 2026-08-01
- Stanford's Mark Horowitz Questions the Future of High-Speed Links and Scaling Trends — jwt0625 · 2026-08-01
- Google's FLARE Paper: 600x Energy Reduction in Attention, Potentially Powering Gemini 4 — dejanseo · 2026-08-01
- SDNQ Quantization Engine Integrated into Diffusers with Multi-Platform Support — RisingSayak · 2026-08-01
- Running 1.6TB Kimi K3 Weights: 128GB Mac vs 80x RTX 5090 Cluster — 机器之心 · 2026-08-01