English(EN) Strata for all Qwen models from little to big

RustingStrata 与 llama.cpp：Qwen 模型性能基准测试

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 20:47

对 RustingStrata 和 llama.cpp 在不同 Qwen 模型上的性能比较显示，llama.cpp 在较小模型上通常提供更快的预填充速度。然而，当模型大小超过可用 VRAM 时，RustingStrata 表现出更优越的性能，在较大模型上预填充速度最高可达 1.5 倍，解码速度最高可达 2 倍。基准测试在一台配备 RTX 3060 12 GB GPU、Ryzen 5 7600X CPU 和 32 GB RAM 的 PC 上进行。 AI

影响根据模型大小和硬件限制，为选择最佳推理引擎提供见解。

排序理由对现有 LLM 推理软件工具的基准测试。[lever_c_demoted from research: ic=1 ai=0.7]

在 r/LocalLLaMA 阅读 →

基础设施

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

本文如何被排名

Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对现有 LLM 推理软件工具的基准测试。[lever_c_demoted from research: ic=1 ai=0.7]
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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

r/LocalLLaMA TIER_1 English(EN) · /u/Serious_Scarcity7818 · 2026-11-05 20:47

Qwen 所有模型，从小到大

<div class="md"><p>Ran every Qwen GGUF on my disk through RustingStrata and llama.cpp, same PC, same file, back to back.</p> <p>PC: RTX 3060 12 GB, Ryzen 5 7600X, 32 GB RAM. CUDA. Normal desktop open (browser, Discord), no tuning, so numbers are lower than in the R…

本文如何被排名

报道来源 [1]

Qwen 所有模型，从小到大

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