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). When autodetection fails (VMs, broken nvidia-smi, passthrough setups): Accepted suffixes: G / GB / GiB , M / MB / MiB , T / TB / TiB (case-insensitive). Start the server: t cycles: Default → Dracula → Solarized → Nord → Monokai → Gruvbox Theme saved to ~/.config/llmfit/theme GPU not detected / wrong VRAM reported nvidia-smi not found but you have an NVIDIA GPU Models show as too_tight but you have enough RAM REST API: test endpoints Apple Silicon: VRAM shows as system RAM (expected) Context length environment variable - Fit tiers : perfect (runs great), good (runs well), marginal (runs but tight), too_tight (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. Q4_K_M, Q5_K_S, mlx-4bit) for your hardware - Providers : Ollama, llama.cpp, MLX, Docker Model Runner