vLLMベースのレシピ導入でQwen 3.8 Flash Nextのベンチマークが向上

r/LocalLLaMA ・ 2026-09-04

原題: Updated my benchmark with a new vLLM based recipe for Qwen 3.8 Flash Next : now up to 98/100 (instead of 91 previously)

AI による要約

I was using: weights https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 with the optimized SGLANG (patched) from https://old.reddit.com/r/BlackwellPerformance/comments/1w04xb7/qwen38_flashnext_on_1x_rtx_pro_6000_171_ts_c1_428/ Now I'm using: weights (AWQ W4A16) from: https://huggingface.co/wtdcode/Qwen3.8-Flash-Next-AWQ-W4A16 PLE (INT4) from: https://huggingface.co/primitive-ai/Qwen3.8-Flash-Next-PLE-quant with vLLM patched with the patch from the same repo cf https://huggingface.co/primitive-ai/Qwen3.8-Flash-Next-PLE-quant#serve the goal was to load both the weights and the n-gram PLE quantized in 4bits either on my sm89 or sm120 devices It's way slower (for my low concurrency usecase) but also a lot better. I was surprised to see such a delta. I reached 98/100 (instead of 91) both with medium and xhigh reasoning (still not useful for this bench). And now it really feels like a hu

この要約は当サイトの AI が生成したものです。正確な内容は 元記事（r/LocalLLaMA）をご確認ください。