FitLLM

Name: FitLLM
Author: run.fitllm

MCP serverMCP

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

by run.fitllmrepository ↗homepage ↗v1.1.0

Is this yours?

This record is unclaimed

Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.

Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"fitllm","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.

Claim this record

Observed by Wellknown

Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability108/108 checks ok

when	check	result	http	latency	detail
6 h ago	mcp initialize	ok	200	725 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
12 h ago	mcp initialize	ok	200	164 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
18 h ago	mcp initialize	ok	200	178 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
25 h ago	mcp initialize	ok	200	154 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
31 h ago	mcp initialize	ok	200	1306 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
2 d ago	mcp initialize	ok	200	201 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
2 d ago	mcp initialize	ok	200	159 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
2 d ago	mcp initialize	ok	200	156 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
2 d ago	mcp initialize	ok	200	171 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools
3 d ago	mcp initialize	ok	200	150 ms	server fitllm 1.1.0 · protocol 2025-06-18 · 3 tools

Tools observed via MCP handshake

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 (we
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
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 co

Declared by sources

What the publisher says

Attributed to the source that supplied each field. Treated as claims, not facts.

No long description beyond the summary.

kind Official MCP Registry

name Official MCP Registry

repoUrl Official MCP Registry

summary Official MCP Registry

version Official MCP Registry

description Official MCP Registry

homepageUrl Official MCP Registry

publisherName Official MCP Registry

Provenance

Where this record came from

Every source is kept verbatim. Field changes are logged as events.

Official MCP Registryrun.fitllm/fitllm

first seen 1 mo ago · fetched 31 h agoopen ↗

Field history (7 recent events)

1 mo ago · declared · summary → "Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only."
1 mo ago · declared · description → "Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only."
1 mo ago · declared · publisherName → "run.fitllm"
1 mo ago · declared · homepageUrl → "https://fitllm.run"
1 mo ago · declared · repoUrl → "https://github.com/click6067-ship-it/fitllm-engine"
1 mo ago · declared · version → "1.1.0"
1 mo ago · declared · protocols → ["mcp"]

This record is unclaimed

Evidence

What the publisher says

Capabilities

Where this record came from