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English(EN) What Card Are You Running, and What tok/s Does It Actually Give You?

AI 硬件性能:用户分享 LLM token 吞吐量数据

作者正在进行一项实验,以衡量运行大型语言模型时各种硬件配置的实际 token 吞吐量。他们要求读者提供有关其显卡、使用的具体模型和量化方法以及在不同并发级别下测得的每秒 token 数的详细信息。目标是将这些用户提交的结果与基于内存带宽和模型大小计算出的理论上限进行比较,旨在了解不同系统的实际性能限制和差异。 AI

影响 为深入了解各种硬件上的实际 LLM 推理性能提供了见解,帮助用户优化其配置。

排序理由 该项目是呼吁社区收集 LLM 性能数据,而不是直接发布公告或研究论文。

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 →

AI 硬件性能:用户分享 LLM token 吞吐量数据

本文如何被排名

Signal score
5 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
该项目是呼吁社区收集 LLM 性能数据,而不是直接发布公告或研究论文。
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) · Jax Sinclair ·

    你用的是什么显卡,它实际能给你带来多少 tok/s?

    <p>Two numbers from two different rooms.</p> <p>My last fourteen pieces here, read at 72 hours or later: 0, 10, 10, 21, 24, 25, 28, 30, 32, 40, 40, 40, 54, and one too new to count. Median 28 views. One of them has zero.</p> <p>xulingfeng's post yesterday: 99 reactions and 62 com…