Breaking 200 tok/s: Dynamic Requant Boosts Local LLM Inference Speed
gajesh · x · 2026-08-07
A developer known as morganmcg has successfully broken the local LLM inference speed barrier using a model named senpai on the OpenHands framework. By specifically targeting the scales on the quants and performing a dynamic requant to a more efficient format, the solution achieved over 200 tokens/s on the Laguna XS 2.1 model. This unorthodox approach offers a new optimization path for high-speed local inference.
More from coding & agent
- Testing Claude Opus with Unity CLI: A Major Breakthrough in 3D Spatial Understanding for Gamedev — chongdashu · 2026-08-07
- Are MCP Servers Becoming Architectural Dependencies? Devs Worry About Portability — dancepeop · 2026-08-07
- Agent Harness Bloat is Real: Minimal Context Cuts Costs and Runs — zainhas · 2026-08-07
- Indie Dev Clones Influencer Voice with VoxCPM, Achieving 3s End-to-End Latency — 面壁智能 · 2026-08-07
- Local Models Output Gibberish in Agent Mode: Why Ability Boundaries Matter — Marblapas · 2026-08-07
- DeepSeek Cuts Agentic Loop Costs 100x Without Quality Loss — bindureddy · 2026-08-07