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English(EN) Two 96 GB Ascend cards crun Qwen3.8-flash-next hardware notes, vLLM work, benchmarks, and what is next

用户在华为Ascend硬件上优化Qwen3.8 Flash-Next

作者 PulseAugur 编辑部 · [1 个来源] ·

一位用户成功地在定制硬件设置上优化了Qwen3.8 Flash-Next模型的推理，该设置配备了两块华为Ascend 310P3卡，每块卡有48GB内存。该配置最初在连贯性和速度方面遇到困难，现在每秒可达到约30-61个token。用户详细介绍了他们在软件栈上的工作，包括对vLLM及其Ascend集成的修改，以克服与驱动程序支持、内存布局和自定义算子相关的挑战。 AI

影响 展示了在专用硬件上成功优化LLM推理，可能降低定制部署的门槛。

排序理由 用户驱动的在非标准硬件上对特定模型的优化。

在 r/LocalLLaMA 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

用户在华为Ascend硬件上优化Qwen3.8 Flash-Next

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Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Tool
用户驱动的在非标准硬件上对特定模型的优化。
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报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/matteiuspi ·

    两张96GB Ascend显卡运行Qwen3.8-flash-next的硬件笔记，vLLM工作，基准测试以及下一步是什么

    <!-- SC_OFF --><div class="md"><p>I have been building a somewhat unusual local inference machine around two Huawei Atlas 300I Duo cards. They are relatively inexpensive, passive, dual-accelerator PCIe cards with 96 GB of device memory apiece. They are also absolutely not drop-in…

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