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🦀 ClawHub

MetriLLM

by @thebluehouse75

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.

Versionv0.2.11

Downloads1,477

Comments1

#legal #gaming

View on ClawHub →

TERMINAL

clawhub install metrillm

📖 About This Skill

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

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

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

💡 Examples