Atomic Agent 成为首个在 GAIA 基准上超越 Hermes 的开源本地 AI 智能体。使用 Qwen-3.6-35b-A3B 模型，在相同预算下解决 37/53 个任务（Hermes 为 31 个），且运行更快。其 TurboQuant 技术将 KV-cache 缩小 6.4 倍并提速 30-50%，使 9B 模型也能完成 52.8% 的任务。
The 1st open-source local agent to surpass Hermes on GAIA benchmark.
Loop design, not model size, drove this whole gap.
@atomicagent_io , a local-first AI agent that runs on your machine, with local or cloud models. Open source on macOS, Windows & Linux.
Both agents drove qwen-3.6-35b-a3b, on the same task budget. Atomic Agent solved 37 of 53 tasks, against 31 for Hermes, and it ran faster too.
Most agent loops get costly because every turn re-sends the whole growing context, and small local models choke on that first.
Atomic Agent keeps turns cheap. One inference returns a grammar-checked JSON array of tool calls, reads run in parallel, results get summarized, and the fixed prompt reuses cached work.
Underneath sits a custom llama.cpp build called TurboQuant. It shrinks the memory cache up to 6.4 times and adds 30-50% more speed through tailored decoding. This is how a 9B model, qwen-3.5-9b, still cleared over half the same tasks at 52.8%.
First local agent to beat Hermes on benchmarks! ✦ runs Qwen, Gemma, Llama via llama.cpp ✦ stable-prefix caching keeps sessions cheap ✦ TurboQuant cuts the KV-cache 6.4× smaller ✦ 37 tasks solved vs Hermes' 31 on GAIA Level 1 Open source on macOS, Windows & Linux 👇在 X 查看被引用的帖子
来源：Rohan Paul · x.com