metrillm

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@latest

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

1. Node.js 20+ — check with node -v
2. Ollama or LM Studio installed and running
   • Ollama: ollama.com, then ollama serve
   • LM Studio: lmstudio.ai, load a model and start the server
3. MetriLLM CLI — install globally:

npm install -g metrillm@latest

Usage

List available models

ollama list

Run a full benchmark

metrillm bench --model $ARGUMENTS --json

This 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 --json

Skips 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 --share

Uploads 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 use
• ttft < 500ms = responsive
• memoryUsedGB vs available RAM = will it fit?

Tips

• Use --perf-only for 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