做本地 AI 智能体或工具调用开发的团队，这个对比直接告诉你：模型大小不是关键，控制能力才是。LFM2.5-8B-A1B 的性价比碾压，值得在本地部署试试。
atomic.chat 在 MacBook Pro M5 Max 64GB 上对比了本地 AI 智能体的工具调用能力。Liquid 的 LFM2.5-8B-A1B 模型（8B 参数，MoE 架构）在规划旅行任务中，成功完成所有 7 次外部工具调用（天气查询、货币转换、邮件和提醒），而 gpt-oss-20b 仅完成 3/7 次。LFM2.5-8B-A1B 运行速度达 266 tok/s，内存占用仅 4.8GB，而 gpt-oss-20b 为 146 tok/s 和 11GB。这表明工具调用本质上是控制问题而非语言问题，更小但训练更聚焦的模型可以胜过更大但泛化的模型。
atomic[.]chat (a desktop app that runs LLMs locall…
atomic[.]chat (a desktop app that runs LLMs locally) ran a very revealing comparison for local AI agents, on a MacBook Pro M5 Max, 64GB.
Liquid’s much smaller LFM2.5-8B-A1B beat gpt-oss-20b by finishing every required tool call, cutting runtime by more than half, and using 4.8GB RAM instead of 11GB.
The task was not normal chat, because the model had to plan a trip by calling outside tools for 3 weather checks, 2 currency conversions, 1 email, and 1 reminder.
The striking part is that LFM2.5-8B-A1B is much smaller in active compute, yet it hit every required call at 266tok/s, while gpt-oss-20b used 11GB RAM, made only 3/7 calls, and ran at 146tok/s.
Now, tool calling is a control problem before it is a language problem.
The model has to preserve a checklist across context, decide when language should stop and action should begin, and resist the temptation to answer as if partial completion were enough.
A smaller mixture-of-experts model with only a fraction of its parameters active can win if its training shaped those control habits more sharply than a larger model’s general fluency did.
- berryxia05-29 17:17原文