community Reddit LocalLLaMA 2026-10-24T15:21 正文 thin 0 words

8 GB GPU, 262K context 3.5 BPW Qwen-Flash-Next Coder 70 T/s using llama.cpp

英文标题:8 GB GPU, 262K context 3.5 BPW Qwen-Flash-Next Coder 70 T/s using llama.cpp

The methodology Qwen-Flash-Next has n-gram, MOE packing above its weight but it's still 6B active params. We exploit this by keeping most MOEs in RAM (Smallest MOEs go in VRAM for speedup) and n-gram on disk - ensure you put the model on your fastest SSD. 8 GB VRAM, 24 GB RAM cost. You can run
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中文

The methodology Qwen-Flash-Next has n-gram, MOE packing above its weight but it's still 6B active params. We exploit this by keeping most MOEs in RAM (Smallest MOEs go in VRAM for speedup) and n-gram on disk - ensure you put the model on your fastest SSD. 8 GB VRAM, 24 GB RAM cost. You can run

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English / 原文

The methodology Qwen-Flash-Next has n-gram, MOE packing above its weight but it's still 6B active params. We exploit this by keeping most MOEs in RAM (Smallest MOEs go in VRAM for speedup) and n-gram on disk - ensure you put the model on your fastest SSD. 8 GB VRAM, 24 GB RAM cost. You can run