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FitLLM

run.fitllm/fitllm · v1.1.0

Tools changed (7 days)

Measured by MCP Station

Last check	Reachable
Checked at	—
Check success 7 / 30 days	—
First-try success 30 days	—
Median response	—
Lookups 30 days	—
Authentication	No auth
MCP version	2025-06-18
Rank score	—
Declared risk	Read-only (all tools declare readOnlyHint)
Capabilities declared	resources, tools
Tool definitions	3 tools · with description 100% · input schema is an object 100% · read/destructive hints 100%

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

streamable-http · https://fitllm.run/api/mcp

claude mcp add --transport http fitllm https://fitllm.run/api/mcp

{
  "mcpServers": {
    "fitllm": {
      "url": "https://fitllm.run/api/mcp"
    }
  }
}

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 thi
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 na
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.

Repository · Website · JSON · Text for agents · Registry updated 2026-09-02