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English(EN) Qwen3.8-Flash-Next: Which Quant to Run From 16GB to 128GB in 2026

Qwen 3.8 Flash Next 模型在各种本地设置下的性能讨论 · 已追踪6个来源

作者 PulseAugur 编辑部 · [7 个来源] ·

Reddit的r/LocalLLaMA子版块的用户正在讨论他们在各种硬件配置和软件优化下运行Qwen 3.8 Flash Next模型的经验。讨论内容涵盖使用YaRN等技术实现13万甚至100万token的高上下文长度，以及优化token生成速度。用户正在尝试不同的量化方法（例如，IQ2_XS、IQ3_S、OQ4E）和推理引擎（例如，oMLX、Strata、Infernix），以在消费级GPU和有限内存上最大化性能。 AI

影响 用户正在分享在消费级硬件上运行大型语言模型的优化技术和性能指标，这可能降低高级AI实验的门槛。

排序理由 该集群由Reddit上关于运行特定开源模型的用户讨论和基准测试组成，而非官方发布或研究论文。

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Qwen 3.8 Flash Next 模型在各种本地设置下的性能讨论 · 已追踪6个来源

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该集群由Reddit上关于运行特定开源模型的用户讨论和基准测试组成，而非官方发布或研究论文。
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7 independent sources
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model release, infra
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报道来源 [7]

  1. Towards AI TIER_1 English(EN) · Ankit Agrawal ·

    Qwen3.8-Flash-Next：2026年16GB至128GB运行哪个量化模型

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/qwen3-8-flash-next-on-strata-which-quant-to-run-from-16gb-to-128gb-in-2026-843778d218a1?source=rss----98111c9905da---4"><img src="https://cdn-images-1.medium.com/max/2600/1*Dehmkz8IlwyBc…

  2. r/LocalLLaMA TIER_1 English(EN) · /u/LearnNTeachNLove ·

    你用什么配置，什么框架？以及如何定制它以运行 qwen 3.8 flash next swift，同时实现高上下文长度和合理的 tok/sec？

    <!-- SC_OFF --><div class="md"><p>As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length and a reasonable amount of tok/sec?</p> </div>…

  3. r/LocalLLaMA TIER_1 English(EN) · /u/chibop1 ·

    OMG！如果你有 64GB 内存的 Mac，请尝试使用 oMLX 来运行 Qwen3.8-Flash-Next-oQ4e-mtp！

    <!-- SC_OFF --><div class="md"><p>I'm genuinely shocked! I was able to run Qwen3.8-Flash-Next-oQ4e-mtp on M3Max 64GB with oMLX!</p> <p>Yes, there are other 3bit quants for 64GB Mac, but 4bit is the lowest quant I'd tolerate. I manually quantized the original model from the Qwen r…

  4. r/LocalLLaMA TIER_1 English(EN) · /u/IngwiePhoenix ·

    有人在使用 Qwen3.8 Flash Next 运行 1M 上下文吗？

    <!-- SC_OFF --><div class="md"><p>So, while doing a search to read the model card again, and because I didn't memorize the huggingface URL, I saw an AI &quot;answer&quot; at the top of the search, stating that while it natively supports a ~244k context, it could go to 1M using Ya…

  5. r/LocalLLaMA TIER_1 English(EN) · /u/zipzak ·

    Qwen Flash Next @ 137 tok/s & 3,497 tok/s Prefill w/ 512k context on a 5090, 192gb ram, Windows Build, comparing Strata and Infernix

    <!-- SC_OFF --><div class="md"><p><strong>Strata vs Infernix on the same model, RTX 5090 — A/B at 262k and 512k context</strong></p> <p>Hi folks, another inference engine post for your feed. I was blown away by Strata, but saw someone else post about Infernix with some wild claim…

  6. r/LocalLLaMA TIER_1 English(EN) · /u/zyxciss ·

    Qwen 3.8 Flash Next-GSQ-RCO-IQ2_XS 在仅有 RTX 3060 12GB + 16GB DDR4 内存下以约 21 tok/s 运行（无门剪枝，100% 位精确）

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1x1tclb/qwen_38_flash_nextgsqrcoiq2_xs_at_21_toks_on_just/"> <img alt="Qwen 3.8 Flash Next-GSQ-RCO-IQ2_XS at ~21 tok/s on just an RTX 3060 12GB + 16GB DDR4 RAM(No gate pruning, 100% bit-exact)" src="https://pr…

  7. r/LocalLLaMA TIER_1 English(EN) · /u/bodhi371 ·

    Qwen3.8-Flash-Next-GSQ-RCO (IQ3_S): 12GB显存+32GB内存+NVME上的约20-30 tok/秒解码和300-90k tok/秒预填充

    <!-- SC_OFF --><div class="md"><p>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) &amp; 300 to ~90 thousand tok/sec prefill @ 131k context, on a custom fork of Strata.<…

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