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FitLLM

R0· no sign-in

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

v1.1.0 · active · repository · website · descriptor JSON · versions

An agent can connect right now, no human step.Remote endpoint, no authentication, reachable on the last probe.
Quality91strong
Trust93of 100
Uptime, 30 days100%
Latency p50331 ms

Connect

8 clients · secrets stay placeholders
{
  "mcpServers": {
    "fitllm": {
      "type": "http",
      "url": "https://fitllm.run/api/mcp"
    }
  }
}
claude mcp add --transport http fitllm https://fitllm.run/api/mcp
[mcp_servers.fitllm]
url = "https://fitllm.run/api/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "fitllm": {
      "type": "remote",
      "url": "https://fitllm.run/api/mcp",
      "enabled": true
    }
  }
}
{
  "mcpServers": {
    "fitllm": {
      "type": "http",
      "url": "https://fitllm.run/api/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=fitllm&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vZml0bGxtLnJ1bi9hcGkvbWNwIn0%3D"
}
{
  "servers": {
    "fitllm": {
      "type": "http",
      "url": "https://fitllm.run/api/mcp"
    }
  }
}
{
  "mcpServers": {
    "fitllm": {
      "httpUrl": "https://fitllm.run/api/mcp"
    }
  }
}
extensions:
  "fitllm":
    enabled: true
    name: "fitllm"
    type: streamable_http
    uri: "https://fitllm.run/api/mcp"
    timeout: 300

Quality 91 / 100strong

protocol75 · weight 25
  • warn
    protocol.modernnewest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
  • n/a
    protocol.statelessonly applies to 2026-07-28 servers
  • pass
    protocol.transportstreamable HTTP
  • n/a
    protocol.list_ttlonly applies to 2026-07-28 servers
authorizationn/a · weight 15
  • n/a
    auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
  • n/a
    auth.secret_in_urlno templated URL
tool hygiene100 · weight 30
  • pass
    tools.descriptionsevery tool has a description
  • pass
    tools.description_lengthdescriptions are concise
  • pass
    tools.schemasevery tool has an object input schema
  • pass
    tools.annotations3 of 3 tools declare readOnlyHint or destructiveHint
  • pass
    tools.directory_hintsevery tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
  • pass
    tools.token_costabout 941 tokens to load every tool
  • pass
    tools.api_dumptools are not a one-to-one API dump
stability100 · weight 15
  • pass
    stability.changes2 tool changes in 30 days
  • pass
    stability.rug_pullno tool changed its meaning under the same name
dependenciesn/a · weight 15
  • n/a
    deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

3 tools, about 941 tokens to load them all · tool set a5edd639caf3 · tools last changed 2026-10-18 · checked 2026-10-20 19:10 UTC

Tool changes

  • check_llm_fitchangedinput schema
  • check_llm_fitchangedinput schema

Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 93 / 100

provenance85
liveness100
freshness85
hygiene100

+repository +dns-namespace +website +reachable +uptime-100% ~updated-48d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remoteauthreachableuptime 30dp50protocollast ok
https://fitllm.run/api/mcp streamable-httpnoneyes100%331 ms2025-11-252026-10-20

Tools (3)

check_llm_fitread-onlyCheck 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).
list_supportedread-onlyList 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_fits_on_hardwareread-onlyRank 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.

Schemas: list_tools.

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Official server.json
{
  "name": "run.fitllm/fitllm",
  "title": "FitLLM",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://fitllm.run/api/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "1.1.0",
  "repository": {
    "url": "https://github.com/click6067-ship-it/fitllm-engine",
    "source": "github"
  },
  "websiteUrl": "https://fitllm.run",
  "description": "Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only."
}