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FitLLM MCP Server

not responding

FitLLM is listed as active in the registry but did not answer our last check. It exposes 3 tools. Last commit 14 Sep 2026.

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

Uptime history 51 days of history · worst day 0%
51 days agonow
9.9%
Uptime 24h
9 of 91 checks
3
Tools
read from the server
422 ms
Response time
average over 24h
8
Stars
last commit 14 Sep 2026

What changed 3

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 23 August 2026. No other catalogue keeps this.

3 Sep a tool changed the parameters it asks for check_llm_fit
2 Sep a tool description was rewritten check_llm_fit
23 Aug a tool description was rewritten list_supported

What the code does

We read the source, 15 h ago · tools taken from the live server · rules 3dff92dd89df

Evidence

Things with no honest explanation: a promise that contradicts the code, code that runs at install time while hiding what it does, data leaving the machine.

Claims read-only, but the code runs commands census/check.mjs:18, bin/detect-hardware.mjs:76
  execFileSync(process.execPath, [join(dir, 'generate.mjs')], {
Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

  execFileSync(process.execPath, [join(dir, 'generate.mjs')], {

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

We found things in this code

Code changes quietly between releases, and nobody reads the diff of a dependency. We do, on every release — watch FitLLM and you get told the day something new turns up.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 6 min ago.

run in your terminal
claude mcp add fitllm --transport http https://fitllm.run/api/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm.run/api/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.fitllm]
url = "https://fitllm.run/api/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm.run/api/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm.run/api/mcp"
    }
  }
}

Available tools 3

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

llm
check_llm_fit
Check whether a specific local LLM fits in the memory of a specific GPU or Apple Silicon Mac. Returns fits/tight/won't-fit verdict with the memory breakdown (weights, KV cache, linear-attention state when present, runtime overhead, reserve), max context, and a concrete fix if it doesn't fit. Use this whenever a user asks anything like "can I run <model> on my <GPU/Mac>?", "will <model> fit in <N>GB?", or "what do I need to run <model>?". Estimates using curated, config-derived architecture fields (MLA, sliding-window, hybrid attention, MoE modeled).
supported
list_supported
List the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Standard text-only HuggingFace transformer configs can also be checked via fitllm.run; unsupported architectures are rejected.
what
what_fits_on_hardware
Rank which popular local LLMs fit on a given GPU or Apple Silicon Mac (at ~4-bit quantization, 8K context) — models that fit come first, biggest first, with max context each. Use when a user asks "what can I run on my <GPU/Mac/N GB>?", "best local model for my machine?", or gives hardware without naming a model.

Endpoints

URLTransportStateLatencyChecked
https://fitllm.run/api/mcp streamable-http answering 88 ms 6 min ago

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9 tools answering

FitLLM — questions

Answers built from our own checks of this server.

What can FitLLM do?
It exposes 3 tools, read directly from the server on our last check. Among them: check_llm_fit, list_supported, what_fits_on_hardware. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is FitLLM working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 9 of 91 checks got a reply (9.9%), average response time 422 ms. The bar chart above shows every period we have measured.
The registry lists FitLLM as active — why does it not respond?
The official MCP registry stores what the author submitted; it does not verify that the server still runs. We check the endpoint ourselves, and this one does not answer. Catalogues that copy the registry without checking will show it as working.
How do I connect FitLLM?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does FitLLM need an API key?
No. FitLLM completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 3 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is FitLLM?
It answers our handshake in 422 ms on average, which is faster than 38% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is FitLLM open source?
Yes — it is published under the MIT licence, written in JavaScript, 8 stars on GitHub and 3 open issues. The source link is on this page, so you can read exactly what it does with your data before you connect it.