Use it with Ollama
Ollama is excellent for pulling and running models. LLMFit helps decide which Ollama models are realistic for the machine.
Compare
LLMFit fills a gap that raw model lists and benchmark charts do not.
Local AI users usually compare four different things without separating their roles: model catalogs, benchmark charts, runtimes, and operational fit analysis. LLMFit lives in the fourth category.
| Type | What it tells you | What it does not tell you |
|---|---|---|
| Model catalog | What models exist and their high-level metadata | Whether they will run well on your specific machine |
| Benchmark leaderboard | How models score on curated tasks | Whether the model is a practical local choice for your hardware |
| Runtime installer | How to run or pull models in a given runtime | Which model family is the right fit to pull in the first place |
| LLMFit | Which models, quantizations, and run modes fit your hardware and operational goal | It does not replace the runtime that actually serves inference |
Use it with llama.cpp
llama.cpp gives you a powerful local runtime. LLMFit helps select quantizations and model sizes that make sense before you configure it.
Use it with MLX
MLX is a strong Apple Silicon path. LLMFit helps decide which MLX-formatted models are viable for the memory and throughput target.
Use it with internal tooling
Serve mode gives your platform an answer it can consume directly instead of hard-coding model rules in a dashboard or scheduler.
What LLMFit is not
It is not a training stack, not a benchmark publisher, and not an inference runtime. It is the missing fit-analysis layer between model choice and runtime execution.
Decision support