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Inferbench

@JoniMartin27

代码仓库

关于 Inferbench

InferBench's MCP server lets coding agents run, serve and benchmark local LLMs (text + image, llama.cpp + Stable Diffusion) on your own hardware on demand. Measures real tokens/sec, picks the optimal quant for your GPU, and exposes a 124-model catalog. Local-first, no cloud requi

#llm#local-llm#benchmark#llama-cpp#inference#image-generation

基本信息

分类

其他

传输方式

stdio

发布者

JoniMartin27

提交者

Jonathan Martin Paez

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "inferbench": {
      "command": "C:\\Users\\<user>\\AppData\\Local\\Programs\\InferBench\\resources\\sidecar\\inferbench-backend.exe",
      "args": [
        "--mcp"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Inferbench?

Inferbench is an MCP server that lets coding agents run, serve, and benchmark local LLMs (text + image, via llama.cpp and Stable Diffusion) on your own hardware. It measures real tokens/second, picks the optimal quantization for your GPU, and exposes a 124-model catalog.

How to use Inferbench?

—

Key features of Inferbench

  • Run, serve, and benchmark local LLMs on demand
  • Measures real tokens per second throughput
  • Selects optimal quantization for your GPU automatically
  • Supports text and image models (llama.cpp + Stable Diffusion)
  • Exposes a catalog of 124 models
  • Operates entirely local-first, no cloud required

Use cases of Inferbench

  • Benchmarking local LLM performance across different quantizations and hardware
  • Running coding agent tasks against locally served text and image models
  • Identifying the best-performing model quantization for a given GPU

FAQ from Inferbench

What models does Inferbench support?

Inferbench supports text models via llama.cpp and image models via Stable Diffusion, with a catalog of 124 models.

Where does data stay and is the cloud required?

Inferbench is local-first and requires no cloud.

What performance metrics does Inferbench measure?

It measures real tokens per second throughput.

评论

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