Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
Scored across 3 tools
The three tools occupy distinct query shapes: check_llm_fit takes a specific model+hardware pair, what_fits_on_hardware takes hardware only and ranks models, and list_supported is a pure lookup. There is mild conceptual overlap since what_fits_on_hardware implicitly answers many 'does it fit' questions, but the 'model named vs. not named' boundary in the descriptions keeps routing clear.
All names use consistent snake_case, which helps, but the verb conventions are mixed: check_llm_fit is verb+noun, list_supported drops the noun object, and what_fits_on_hardware is an interrogative phrase. Readable, but not a predictable pattern across the set.
Three tools is lean for a specialized LLM-fitting checker, and each one earns its place (single check, enumeration of known names, hardware-driven ranking). It borders on thin, but nothing feels redundant or missing for the stated narrow scope.
The surface covers the core lifecycle: discover supported names, check a specific model/hardware pair, and rank models for given hardware, with fit verdicts returning breakdowns and fixes. Minor gaps exist, such as no explicit multi-model comparison or context-length/quantization query tool, but agents can work around these via the existing checks.
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 this whenever a user asks anything like "can I run on my <GPU/Mac>?", "will fit in GB?", or "what do I need to run ?". Estimates using curated, config-derived architecture fields (MLA, sliding-window, hybrid attention, MoE modeled).
Inputs: model (required), ctx, env, gpu, +5 more.
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.
Inputs: no named parameters.
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 naming a model.
Inputs: gpu, gpu_count, mac_ram_gb.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.
Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.
Read-only: which local LLMs a GPU or Mac can run - VRAM fit, tokens/sec, model specs.
LLM serving capacity planner: VRAM, KV cache, GPU topology, latency, and cost.
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