Categories › AI & Agents › Models, LLM APIs, routing, serving, training & GPUs
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
Remote server: your requests and data go to fitllm.run. Claude Code: claude mcp add --transport http fitllm https://fitllm.run/api/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
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 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, biglist_supported — List the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Standard text-only HugginNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.