Qwen· @Alibaba_Qwen · X·· 2026-08-14AI 评分45
AI 导读
通义千问发布 Qwen3.8-27B,采用与 2.4T 旗舰相同的混合骨干架构,但为稠密而非 MoE,单张 Blackwell GPU 即可运行。原生 262K 上下文可扩展至 1M,GB300 上可同时处理 6 条全长序列;MTP 草稿头内置于检查点,短提示词接受率 BF16 达 92.2%、FP8 达 84.8%。
正文 · 原文
One GPU, 1M context, Day-0 ready. Big props to the vLLM team for the seamless integration!👍
Try Qwen3.8-27B on vLLM: @vllm_project
https://recipes.vllm.ai/Qwen/Qwen3.8-27B
🎉 Qwen3.8-27B is here from @Alibaba_Qwen, and the whole thing fits on a single GPU. Same hybrid backbone as the 2.4T flagship, dense instead of MoE. Day-0 support in vLLM. 🚀 What is in it for serving ✨ - Fits one Blackwell GPU in every precision. Qwen ships BF16 and FP8, the NVFP4 build from @inferact - 262K native context, stretching to 1M. At that length one GB300 still has room for roughly 6.6M KV tokens. Six full-length sequences in flight, on one GPU - An MTP draft head rides inside the checkpoint, so speculative decoding needs no separate speculator repo. Acceptance on short prompts measured 92.2% in BF16 and 84.8% in FP8 Verified end-to-end on @NVIDIA GB300: BF16 and FP8 at TP4, NVFP4 at TP1, tool calls working, correct generations at 1M context. Two prerequisites, vLLM nightly and transformers 5.8.0+. 🔗 http://recipes.vllm.ai/Qwen/Qwen3.8-27B在 X 查看被引用的帖子
来源:Qwen · x.com