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English(EN) I put the 381 tok/s Qwen stack on one RTX 3090, real chat does 118 and the headline mode crashes

HyperQwen 堆栈在 RTX 3090 上达到 118 tok/s,但高性能模式崩溃

一位开发者在一块 RTX 3090 上测试了 HyperQwen 堆栈,在聊天时达到了 118 token/秒,在逐字引用上下文时达到了 237 token/秒。与在相同模型和硬件上使用 llama.cpp 达到的 42 token/秒基线相比,这一性能显著提升。然而,声称高达 381 token/秒的更高性能模式在尝试处理大上下文大小时崩溃了,这表明了推测解码实现存在不稳定性。 AI

影响 展示了在消费级 GPU 上可实现的推理速度,但突出了高级推测解码技术的不稳定性。

排序理由 在消费级硬件上对 LLM 推理堆栈进行开发者测试和性能基准测试。

在 dev.to — LLM tag 阅读 →

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

HyperQwen 堆栈在 RTX 3090 上达到 118 tok/s,但高性能模式崩溃

本文如何被排名

Signal score
38 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
在消费级硬件上对 LLM 推理堆栈进行开发者测试和性能基准测试。
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, product
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
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · prodbymarcu ·

    我将 381 tok/s 的 Qwen 堆栈放在了一张 RTX 3090 上,实际聊天速度为 118,而标题模式崩溃了

    <p>A post went around earlier this month, 82 to 381 tok/s on a single RTX 3090 running a 27B model. I have that card and it's never idle, a coder model lives on it for my agent sessions, so I installed the stack and measured it on my own bench. Twice, because the first attempt di…