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llmfit

Detects your machine's CPU/RAM/GPU and ranks which LLMs will run well: estimates memory fit, speed, quality, and context, offers an interactive TUI and CLI, and lets you run and share reproducible local benchmarks.

Introduction

Most LLM compatibility tools either guess from parameter counts or force you to actually run every model. llmfit takes a different approach: it detects your hardware and combines empirical runtime sampling, community measurements, and analytic estimates to tell you which models will realistically fit and perform on your system — before you download them.

What Sets It Apart
  • Practical fit-first ranking: scores models across memory fit, estimated throughput, quality, and context needs so you can pick a model that actually runs on your rig rather than one that only looks good on paper.
  • Measured estimates + community leaderboard: ship-with estimates are grounded in sampled runtime data and user-submitted benchmarks; contributors can run a local benchmark and submit results so future users get measured tok/s numbers for identical hardware.
  • Multi-provider runtime awareness: understands local providers and runtimes (llama.cpp, Ollama, MLX, Docker Model Runner, LM Studio), multi-GPU setups, MoE models, and dynamic quantization options — it reports assumptions and verification commands for transparency.
  • Dual UX for different workflows: an interactive TUI for exploration and planning, and a JSON-friendly CLI for automation, agents, and scripting.
Who It's For / Trade-offs

Great fit if you want to evaluate or deploy local LLMs without trial-and-error downloads, especially on limited VRAM or multi-GPU machines, or if you run reproducible benchmarks that should be shared across similar hardware. It helps ops engineers, hobbyists running models locally, and teams deciding which model to standardize on.

Look elsewhere if you need a hosted model-serving control plane, in-depth model fine-tuning tooling, or a conversational client — llmfit focuses on sizing, fit, and performance estimation rather than model training or full-serving orchestration. Its accuracy depends on correct hardware detection and the representativeness of runtime providers you use; for absolute certainty, follow up with the built-in bench command on your target runtime.

Information

  • Websitegithub.com
  • AuthorsAlexsJones
  • Published date2026/02/15

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