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English(EN) Rented some server time and wanted to share some learnings from benching using RAM heavier servers with some decent GPU: AMD EPYC and ROME motherboards really *ARE* worth it: Qwen 3.8 Flash Next Unsloth Q8 at 94 tok/s c=1, 456 tok/s c=8 .

AMD EPYC & ROME 服务器在大语言模型方面表现强劲

作者 PulseAugur 编辑部 · [1 个来源] ·

一位Reddit用户分享了在租用的服务器硬件上运行大语言模型的性能基准测试结果，特别强调了AMD EPYC和ROME主板的有效性。这些测试花费约40美元，表明即使使用Qwen 3.8 Flash Next Unsloth Q8等内存密集型模型，该配置也能在并发流高达至少8个时实现近乎线性的性能扩展。 AI

影响 建议特定的服务器硬件配置可以显著提高LLM推理性能和可扩展性。

排序理由 用户生成的关于运行LLM的硬件配置的基准测试结果。

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 →

AMD EPYC & ROME 服务器在大语言模型方面表现强劲

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
用户生成的关于运行LLM的硬件配置的基准测试结果。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
2 days old
Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/sleight42 ·

    Rented some server time and wanted to share some learnings from benching using RAM heavier servers with some decent GPU: AMD EPYC and ROME motherboards really *ARE* worth it: Qwen 3.8 Flash Next Unsloth Q8 at 94 tok/s c=1, 456 tok/s c=8 .

    <!-- SC_OFF --><div class="md"><p>Raw data in the comments, if you go for that sort of thing.</p> <h1>The TL;DR</h1> <p>This build generates a nearly linear performance per stream up to <em>at least</em> 8 streams using a context window of 124k. I didn't test beyond that.</p> <h1…

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