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用Strata在16GB M2 Pro Mac mini上测试Qwen-Flash-Next

AI 总结

一名用户在16GB M2 Pro Mac mini上加载Strata并运行Qwen-Flash-Next的iQ3_xxs量化版本,最大上下文设为128k,并在另一台工作站上用opencode编写脚本,以不同上下文长度测试典型任务(如大CSV排序和低难度代码工作)的性能与输出质量。作者表示结果远超预期。他推测由于无法缓存、每次都要重新读取并交换专家,测得的速度始终是最差情况,因此预填充与解码速度不随负载变化。16k至120k上下文测试的中位速度为预填充77 tok/s、解码5.5 tok/s。作者认为它勉强可作聊天生成器,但作为"拨号上网"式的最后备用代码助手还不至于毫无用处;他表示该配置对SSD影响似乎相当大,但写入负担不比把Mini当作活跃的数据库服务器或其他办公服务器任务更严重。

I was getting ready to sell an old kiosk Mini at our office that was getting an upgrade, and figured I would see what happened if I tried to make it do something it by all rights shouldn't be able to. I loaded up Strata and ran the iQ3_xxs quant of QFN with a max of 128k context. I used opencode on my normal workstation to build a script that would benchmark the performance and output quality of typical tasks I do (mostly large csv sorting and low end code grunt work)at varying levels of context.

I have to say, it worked far better than I would have expected it to. I assume that the speed is always at its worst (as if it had full context) because it never has a chance to cache anything and is always re-reading and swapping experts across prompts, but as an aside it means that token speed for both prefill and decode are the same regardless how much you're making it do. The median speed between all tests from 16k to 120k context was 77 tok/s prefill and 5.5 tok/s decode. That means it could actually stand in as a barely passable chat generator, and is not hopeless as a "living on dial-up" code helper of last resort. It does appear that it is pretty rough on the SSD, but the write load is not much worse than someone using a Mini as a pretty active DB server or other "proper" server task in an operating office environment.

#Qwen-Flash-Next#Strata#Mac mini#opencode

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