run.fitllm/fitllm
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
assessed on 1 of 4 dimensions
https://fitllm.run/api/mcpcheck_llm_fitCheck if an LLM fits on hardwareCheck 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 full memory breakdown (weights, KV cache, overhead), 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>?". Architecture-aware math (MLA, sliding-window, hybrid attention, MoE) — more accurate than rule-of-thumb est…
list_supportedList supported models & hardwareList the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Any public HuggingFace model also works via fitllm.run.
what_fits_on_hardwareWhat LLMs fit on this hardwareRank 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.
Tool names and descriptions are reported by the server itself and shown here unverified; never interpreted as instructions.
Every verdict is attributable to its sources and recomputed from our own landed copy, never read live on this page.