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How to run a local model behind an OpenAI-compatible API in one command with Mesh LLM

Install Mesh LLM, let it pick a model and backend, and point any OpenAI client at localhost.

Short answer: Install with curl -fsSL https://raw.githubusercontent.com/Mesh-LLM/mesh-llm/main/install.sh | bash; then Finish setup with mesh-llm setup; start serving with mesh-llm serve --auto. All steps.

Remote script. The quick start pipes install.sh into bash; use the Homebrew formula on Apple Silicon if you prefer.

Who it is forA developer who wants a local endpoint for apps that already speak the OpenAI API.

The problemSetting up a backend, downloading a model and exposing an API by hand takes several tools.

The outcomeA local /v1 API on port 9337 and a web console on port 3131, ready for chat completions.

What you need first

  • macOS, Linux or Windows (install script, PowerShell or Homebrew on Apple Silicon)
  • One or more machines with a GPU or enough memory for the model
  • curl and jq for the README's test requests

Steps

  1. Install with curl -fsSL https://raw.githubusercontent.com/Mesh-LLM/mesh-llm/main/install.sh | bash
  2. Finish setup with mesh-llm setup
  3. Start serving with mesh-llm serve --auto
  4. List models with curl -s http://localhost:9337/v1/models | jq '.data[].id'
  5. Send a chat completion request to http://localhost:9337/v1/chat/completions from your app

Prompt for your agent

Install Mesh-LLM/mesh-llm on this machine, run setup and serve --auto, list the available models, and send one test chat completion to localhost:9337 so I can point <app> at it.

Caveat. This card is written from the Mesh-LLM/mesh-llm README, not from a test run by Deduped. Before you start, make sure you have macOS, Linux or Windows. Check the repo for newer instructions before you rely on these steps.

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Using the Deduped MCP, get the setup steps for Mesh-LLM/mesh-llm and "Run a local model behind an OpenAI-compatible API in one command"

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