{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "mesh-llm-mesh-llm--serve-a-local-model-through-an-openai-compatible-api",
 "repo": "Mesh-LLM/mesh-llm",
 "repo_url": "https://github.com/Mesh-LLM/mesh-llm",
 "repo_slug": "mesh-llm-mesh-llm",
 "title": "Run a local model behind an OpenAI-compatible API in one command",
 "page_title": "How to run a local model behind an OpenAI-compatible API in one command with Mesh LLM",
 "summary": "Install Mesh LLM, let it pick a model and backend, and point any OpenAI client at localhost.",
 "who": "A developer who wants a local endpoint for apps that already speak the OpenAI API.",
 "problem": "Setting up a backend, downloading a model and exposing an API by hand takes several tools.",
 "outcome": "A local /v1 API on port 9337 and a web console on port 3131, ready for chat completions.",
 "steps": [
  "Install with `curl -fsSL https://raw.githubusercontent.com/Mesh-LLM/mesh-llm/main/install.sh | bash`",
  "Finish setup with `mesh-llm setup`",
  "Start serving with `mesh-llm serve --auto`",
  "List models with `curl -s http://localhost:9337/v1/models | jq '.data[].id'`",
  "Send a chat completion request to http://localhost:9337/v1/chat/completions from your app"
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Beginner",
 "time": "20 minutes",
 "basis": "readme",
 "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.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "rust",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "remote-script",
   "notice": "The quick start pipes install.sh into bash; use the Homebrew formula on Apple Silicon if you prefer."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/mesh-llm-mesh-llm/serve-a-local-model-through-an-openai-compatible-api/",
 "md": "https://dedupedai.com/use-cases/mesh-llm-mesh-llm/serve-a-local-model-through-an-openai-compatible-api/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/mesh-llm-mesh-llm--serve-a-local-model-through-an-openai-compatible-api.json",
 "api_version": "v1"
}