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4x3090-llm-benchmarks

alesha-pro

一个公开存档的数据集,包含来自一台4×RTX 3090家用机器的638次LLM推理基准测试,每次运行都记录完整元数据(包括精确启动命令),可通过SQLite、JSONL和Markdown目录查询。

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最近更新

2026-07-28

AI 仓库情报摘要
FR-AI / ANALYSIS

为什么值得关注

该仓库提供了异常详细且可复现的基准测试存档,保留了每次运行的模型检查点、量化方式、KV缓存类型、张量并行布局、引擎构建及启动命令,使得仅凭单个令牌/秒数字无法实现的精确交叉比较与分析成为可能。

适合谁使用

  • LLM推理基准测试爱好者
  • 在消费级硬件上优化多GPU LLM部署的工程师
  • 比较量化方法或引擎性能的研究人员
  • 开发或调优vLLM、llama.cpp等运行时的开发者

典型使用场景

  • 在同一模型和硬件上比较不同量化格式(如AutoRound与AWQ)的推理吞吐量
  • 为特定工作负载(如单流 vs. 高并发)确定最佳张量并行度和功耗限制
  • 分析不同上下文长度和KV缓存数据类型下的缓存效率
  • 通过检查精确启动命令和工件来复现或验证已发表的基准测试声明

项目优势

  • 638次测量中有637次附带了精确的启动命令(argv或shell命令),确保可复现性
  • 数据以多种可查询格式(SQLite、JSONL)存储,并附有模式文档
  • 涵盖14个模型家族、20个基准测试活动和23种量化类别,包括vLLM、llama.cpp和实验性的exllamav3
  • 每行记录都记录了运行时细节(引擎版本、注意力后端、KV缓存数据类型、GPU布局、功耗限制),这些是解释指标所必需的

使用前须知

  • 不是排行榜;不同活动之间的测试框架和变量会发生变化,因此只有同一活动内的行之间才能直接比较
  • 基于单一的硬件配置(4×RTX 3090,PCIe 3.0,无NVLink),限制了结论推广到其他配置
  • 数据集更新是手动的且不频繁;仅在作者审查并推送新快照时发布

README 快速开始

4x RTX 3090 LLM benchmark archive

638 local inference measurements from one 96 GB home rig. The exact launch command is attached to 637 of them.

Most benchmark posts give you a model name and one tok/s number. I kept losing the rest: which checkpoint, which quant, which KV dtype, which TP layout, which engine build, and whether the number meant single-stream decode or aggregate throughput.

This repository is the database I wanted. You can inspect one result in the Markdown catalog, query all of them in SQLite, or take the JSONL and run your own analysis.

Navigation

OpenWhat is there
ModelsAll 14 model families and their runs
Benchmark campaignsMatched experiments and test matrices
EnginesvLLM, llama.cpp, and the experimental exllamav3 slice
Query guideReady-to-run SQLite, JSONL, and Python examples
MethodologyMetric definitions, comparison rules, and limitations
HardwareThe 4x RTX 3090 rig, topology, power limits, and software versions
Dataset filesJSONL, SQLite, snapshot metadata, and schema notes

The archive currently has 638 measurements from 20 benchmark campaigns across 14 model families. Most runs use vLLM. The llama.cpp set is smaller, and the 38 exllamav3 rows came from a short experiment rather than broad engine coverage.

This is a benchmark archive, not a leaderboard. The harness and the variable I was testing changed between campaigns, so some rows should not be compared directly. Each row keeps its workload, topology, power limit, engine version, source references, and launch command when available. 637 of 638 measurements retain an exact argv or command.

What makes one row useful

Every result keeps the context needed to understand or reproduce it:

Recorded with the numberExamples
Model artifactcheckpoint, quantization recipe, source revision
Runtimeengine, version, attention backend, KV-cache dtype
GPU layoutTP, PP, DP, instance count, power limit, P2P state
Workloadprompt length, output length, concurrency, context limit
Metric meaningsingle-stream wall rate, decode rate, aggregate output, TTFT
Receipt

项目描述

Local LLM speed measurements from a 4x RTX 3090 rig, with exact launch commands and per-run provenance.

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