llm_speedtest — Agent Tool by gengchaogit

by gengchaogit · Agent Tool · ★ 97

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About llm_speedtest

🚀 本地大模型推理速度测试工具 v3 (chao魔改版) 🇬🇧 View in English (查看英文版) | 🇨🇳 查看中文版 ✨ 在线体验最新纯前端版(无需下载/部署,点开即用): 👉 测速页面: https://gengchaogit.github.io/llmspeedtest/ 👉 全网排行榜: https://gengchaogit.github.io/llmspeedtest/LLMSpeedTestv3Leaderboard.html 简介 本项目是一个功能强大的本地大模型推理性能测试工具,旨在帮助用户快速、便捷地测试本地部署的各种大模型推理服务的 Prefill(预填充)和 Decode(解码/输出)性能。 提供两种版本: 纯前端版本 (v3最新):通过浏览器本地离线运行,全新升级支持直接连接Supabase云端,实现测试数据上传、专属排行分享词、全网总榜与CSV批量上传,零部署成本! Python后端版本 (v3):通过Python后端发起请求,突破浏览器6并发限制,支持真正的高并发测试(50+并发),同步支持天梯榜。 本工具基于原作者纸鸢随风(B站)的作品进行魔改,增加了多项实用功能和优化,包括重试机制、图表自动显示、Prefill 和 Decode 速度分离、并发问题修复、请求超时控制、配置自动保存、并发吞吐修复、Token来源追踪等。 🔥 重要提示 ### 推荐使用 Python后端版本 🐍 Python后端版本功能更完善,维护更及时! #### ✅ Python后端版本优势(v3最新) - 🏆 独家天梯榜: 将测试结果上传至全网排行榜,获取专属分享链接 - 🚀 真实高并发:突破浏览器6并发限制,支持50+并发 - 📊 完整6图表体系:Prefill/Decode吞吐 + TTFT/ITL延迟 + 百分位统计 - 🔍 TTFT与ITL测量:首Token延迟 + Token间延迟(平均值+标准差/抖动) - 📈 全维度对比:历史记录对比支持6个图表,全方位性能分析 - 💾 增强导出:CSV包含全部数据,支持Excel无乱码,一键导出所有图表 - 🎯 更准确的吞吐计算:基于真实时间戳的并发总吞吐量 - ⚡ 积极维护:新功能优先在Python版本实现 #### ⚠️ 浏览器版本限制 - 并发限制:最多6并发(浏览器限制) - 测试准确性:最高并发下受浏览器网络调度限制较大 📖 快速开始: Python后端版本完整文档 💡 推荐流程: 1. 双击 python/启

Quick Facts

Stars97
Forks9
LanguageHTML
CategoryAgent Tool
Quality Score53.789323343007/100
Open Issues1
Last Updated2026-07-02
Created2025-11-08
Est. Tokens~19k

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Frequently Asked Questions

What is llm_speedtest?

llm_speedtest is Local LLM Inference Speed Test Tool. It is categorized as a Agent Tool with 97 GitHub stars.

What programming language is llm_speedtest written in?

llm_speedtest is primarily written in HTML.

How do I install or use llm_speedtest?

You can find installation instructions and usage details in the llm_speedtest GitHub repository at github.com/gengchaogit/llm_speedtest. The project has 97 stars and 9 forks, indicating an active community.

What are the best alternatives to llm_speedtest?

The top alternatives to llm_speedtest on Agent Skills Hub include claude-skills-marketplace, tutor-skills, lenny-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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