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Inferbench

@JoniMartin27

About 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

Basic information

Category

Other

Transports

stdio

Publisher

JoniMartin27

Submitted by

Jonathan Martin Paez

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"
      ]
    }
  }
}

Tools

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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

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.

Comments

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