- name
- metrillm
- description
- Find the best local LLM for your machine. Tests speed, quality and RAM fit, then tells you if a model is worth running on your hardware.
- argument-hint
- [model-name]
- author
- MetriLLM
- source
- https://github.com/MetriLLM/metrillm
- license
- Apache-2.0
- allowed-tools
- Bash, Read
- install
- npm install -g metrillm
MetriLLM — Find the Best LLM for Your Hardware
Test any local model and get a clear verdict: is it worth running on your machine?
Prerequisites
- Node.js 20+ — check with
node -v - Ollama or LM Studio installed and running
- Ollama: ollama.com, then ollama serve - LM Studio: lmstudio.ai, load a model and start the server
- MetriLLM CLI — install globally:
npm install -g metrillmUsage
List available models
ollama listRun a full benchmark
metrillm bench --model $ARGUMENTS --jsonThis measures:
- Performance: tokens/second, time to first token, memory usage
- Quality: reasoning, math, coding, instruction following, structured output, multilingual
- Fitness verdict: EXCELLENT / GOOD / MARGINAL / NOT RECOMMENDED
Performance-only benchmark (faster)
metrillm bench --model $ARGUMENTS --perf-only --jsonSkips quality evaluation — measures speed and memory only.
View previous results
ls ~/.metrillm/results/Read any JSON file to see full benchmark details.
Share to the public leaderboard
metrillm bench --model $ARGUMENTS --shareUploads your result to the MetriLLM community leaderboard — an open, community-driven ranking of local LLM performance across real hardware. Compare your results with others and help the community find the best models for every setup. Shared data includes: model name, scores, hardware specs (CPU, RAM, GPU). No personal data is sent.
Interpreting Results
| Verdict | Score | Meaning |
|---|---|---|
| EXCELLENT | >= 80 | Fast and accurate — great fit |
| GOOD | >= 60 | Solid — suitable for most tasks |
| MARGINAL | >= 40 | Usable but with tradeoffs |
| NOT RECOMMENDED | < 40 | Too slow or inaccurate |
Key metrics to highlight:
tokensPerSecond> 30 = good for interactive usettft< 500ms = responsivememoryUsedGBvs available RAM = will it fit?
Tips
- Use
--perf-onlyfor quick tests - Close GPU-intensive apps before benchmarking
- Benchmark duration varies depending on model speed and response length
Open Source
MetriLLM is free and open source (Apache 2.0). Contributions, issues, and feedback are welcome: github.com/MetriLLM/metrillm