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Inferbench MCP Server

@JoniMartin27

Repository

Basic information

Category

Other

Transports

stdio

Author

JoniMartin27

Submitted by

Jonathan Martin Paez

About Inferbench MCP Server

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

Inferbench MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Inferbench MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

1

What models does Inferbench support?

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

2

Where does data stay and is the cloud required?

Inferbench is local-first and requires no cloud.

3

What performance metrics does Inferbench measure?

It measures real tokens per second throughput.

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