Qwen3.8-Flash-Next-GSQ-RCO (IQ3_S): ~20-30 tok/sec decode & 300-90k tok/sec prefill on 12GB VRAM + 32GB RAM + NVME

Reddit r/LocalLLaMA Models

Summary

用户在仅 12GB 显存 + 32GB 内存 + NVMe 的消费级硬件上,通过自定义 Strata 分支运行 IQ3_S 量化版 Qwen3.8-Flash-Next-GSQ-RCO-Abliterated,实现 20-30 tok/s 解码与最高约 90k tok/s 的 131k 上下文预填性能。

I got Qwen3.8-Flash-Next-GSQ-RCO-Abliterated running at IQ3_S with just 12GB VRAM and 32GB system RAM, achieving 20-30 tok/sec decode (Q2 achieves 39-45 tok/s) & 300 to ~90 thousand tok/sec prefill @ 131k context, on a custom fork of Strata. This fork has tonnes of architectural changes, all are very experimental and will probably break. But the performance makes up for it. This feels like local Opus in some regards, on sub 2k in compute. https://github.com/bodhi37/Qwen3.8-Flash-12GBRAM-32GBVRAM-SSD-Recipe https://github.com/bodhi37/strata
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Qwen3.8-Flash-Next (125B) at 12-15 tok/s on a 2021 32GB M1 Max

Reddit r/LocalLLaMA

MoEspresso 3, an inference engine, achieves 12-15 decode tokens per second for the Qwen3.8-Flash-Next model on a 2021 M1 Max with 32GB RAM, using techniques like Cache-Prior to optimize performance and outperforming some hosted solutions in benchmarks.