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Catalog › run.fitllm/fitllm

Is FitLLM reliable?

Not measured yet Not measured yet. Faceabot will test it automatically; you can speed this up with the MCP tool check_reliability.

En français : Pas encore mesuré. Faceabot teste cet outil lui-même, en continu, sans dépendre de ce que son auteur affirme.

What it does

Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.

Source: official MCP registry (registry.modelcontextprotocol.io) · version 1.1.0 · publisher website
Connect your agent
{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm.run/api/mcp"
    }
  }
}
Or ask Faceabot first: MCP tool check_reliability {"id":"run.fitllm/fitllm"} on https://faceabot.com/api/mcp.
Does it actually do the job?

Not tested with a real task yet. Faceabot only runs real tasks on tools that declare themselves read-only (readOnlyHint), with a neutral sample input.

Faceabot measurements

No measurement yet.

Method: MCP initialize then tools/list from Faceabot (FaceabotProbe/1.0), plus one read-only tool call with a neutral input when the server declares one, repeated over time. This checks that the server is up and speaks MCP correctly; it does not certify the quality or safety of each tool. How measurement works.

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Publisher of FitLLM?

Show your measured reliability on your README or website:

Faceabot reliability badge for FitLLM

[![Measured by Faceabot](https://faceabot.com/badge/run.fitllm/fitllm.svg)](https://faceabot.com/tools/run.fitllm/fitllm)
Data in JSON: /api/catalog?id=run.fitllm/fitllm · Stop probes: POST /api/reliability/opt-out

AI agents: all of this is available without keys over MCP https://faceabot.com/api/mcp · HTTP https://faceabot.com/api/catalog?q=… · Guide : llms.txt

Is your store ready for AI shopping agents? · State of MCP (weekly) · MCP server doctor · Open reliability data