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English(EN) Getting incredible results with a single RTX 3090ti and 128gb of ram. Deepseek-V4.1-Flash-1017 (35 tok/s decode) and Qwen3.8-Flash-Next (130 tok/s decode)

RTX 3090 Ti 通过自定义 GPU 解锁实现高 LLM 速度

Reddit 的 r/LocalLLaMA 子版块的一位用户分享了他们在消费级硬件上实现本地大型语言模型高性能的设置。通过使用单个 RTX 3090 Ti GPU 和 128GB RAM,他们能够以大约每秒 35 个 token 的速度运行 DeepSeek-V4.1-Flash-1017,以大约每秒 130-140 个 token 的速度运行 Qwen3.8-Flash-Next。该用户详细介绍了一种使用名为 `gpu-unlock` 的自定义内核模块的方法,以绕过 NVIDIA 消费级 GPU 上的人工固件节流,从而实现更快的矩阵指令发行速率。 AI

影响 通过硬件优化,在消费级 GPU 上实现更高的本地 LLM 性能。

排序理由 用户生成的关于优化消费级硬件以获得 LLM 性能的指南。

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 →

RTX 3090 Ti 通过自定义 GPU 解锁实现高 LLM 速度

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用户生成的关于优化消费级硬件以获得 LLM 性能的指南。
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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, model release
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High
Clearly on-topic for AI-industry coverage.
Story freshness
1 days old
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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/uniVocity ·

    Getting incredible results with a single RTX 3090ti and 128gb of ram. Deepseek-V4.1-Flash-1017 (35 tok/s decode) and Qwen3.8-Flash-Next (130 tok/s decode)

    <!-- SC_OFF --><div class="md"><p>I'm sharing this here because this is working way better than it used to a few months ago with older models. I see people here posting slower speeds with much better hardware than mine and hopefully this information helps.</p> <p>I used gemini-fl…