Agent skill · research science · aradotso
llmfit-hardware-model-matcher
Terminal tool that detects your hardware and recommends which LLM models will actually run well on your system
Why this skill is useful
Provides domain-specific knowledge on hardware compatibility for LLM models that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, llmfit installed locally. About 7k tokens when loaded. Last updated 2026-07-09. 70 stars on the source repository.
What this skill does
llmfit Hardware Model Matcher Skill by ara.so — Daily 2026 Skills collection. llmfit detects your system's RAM, CPU, and GPU then scores hundreds of LLM models across quality, speed, fit, and context dimensions — telling you exactly which models will run well on your hardware. It ships with an interactive TUI and a CLI, supports multi-GPU, MoE architectures, dynamic quantization, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner). --- Installation macOS / Linux (Homebrew) Quick install script Windows (Scoop) Docker / Podman From source (Rust) --- Core Concepts Fit tiers: perfect (runs great), good (runs well), marginal (runs but tight), tootight (won't run) Scoring dimensions: quality, speed (tok/s estimate), fit (memory headroom), context capacity Run modes: GPU, CPU+GPU offload, CPU-only, MoE Quantization: automatically selects best quant (e.g. Q4KM, Q5KS, mlx-4bit) for your hardware Providers: Ollama, llama.cpp, MLX, Docker Model Runner --- Key Commands Launch Interactive TUI CLI Table Output Show System Hardware Detection List All Models Search Models Fit Analysis Model Detail Recommendations Hardware Planning (invert: what hardware do I need?) REST API Server (for cluster scheduling) --- Hardware Overrides When autodetection fails (VMs, broken nvidia-smi, passthrough setups): Accepted suffixes: G/GB/GiB, M/MB/MiB, T/TB/TiB (case-insensitive). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/llmfit-hardware-model-matcher