If you’re stuck on one llm engine assuming it’s the best, check again
Tested qwen 3.8 27b on 3090 (350w, 64k ctx) with 4 serving engines hyperqwen: fastest at everything except loading. 180 tok/s avg, and 448 tok/s total at 8 users tensorfold: same exl3 files as exllamav3 but
Like GPT-6 Astra, Qwen 3.8 27B can interpret model weights
Treat the mechanism as a merge gate: prove it in CI before the next demo reel.
A study showed that AI raised the math score of a small model from 7.9% to 48.1% by directly modifying the weights without learning.
But Banban and Jaehyun came out in one day. If you look at the conditions, the story is a little different. How much was really 'just looking at the weights' and how much was Python and model execution? It's organized in numbers 👇 The original text is MIT CSAIL
Grok Bot is very useful, but the quota is too small. After building more than ten Bots, it is obviously not enough.
Open Bot can connect its own large model, so the degree of freedom is much higher. Grok Bot can also call Open Bot to execute it. Grok Bot + Open Bot + QWen 3.8 flash next, try today to see if this approach works.
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