EnviroLLM CLI

A command-line tool for benchmarking energy consumption and performance of local LLMs across Ollama, LM Studio, vLLM, and other platforms.

Installation

Run with npx (no installation needed):
``bash
npx envirollm start
`

Or install globally:
`bash
npm install -g envirollm
`

Requirements

- Node.js 14+
- Python 3.7+
- pip (Python package manager)

Quick Start

1. Start the backend service:
`bash
npx envirollm start
`

2. Run benchmarks:
`bash
# Ollama models
npx envirollm benchmark --models llama3:8b,phi3:mini

# LM Studio or other APIs
npx envirollm benchmark-openai --url http://localhost:1234/v1 --model llama-3-8b
`

3. View results:
- CLI: Results displayed in terminal after benchmark completes
- Web: envirollm.com/optimize for more detailed UI

Commands

`bash

Service Management

npx envirollm start # Start backend service (required for benchmarks)
npx envirollm start --port 8002 # Start on custom port
npx envirollm status # Check if service is running

Benchmarking - Ollama

npx envirollm benchmark --models llama3:8b,phi3:mini
npx envirollm benchmark --models llama3:8b --prompt "Write a sorting function"
npx envirollm benchmark --models llama3:8b,llama3:8b-q8 # Compare quantizations

Benchmarking - LM Studio, vLLM, Custom APIs

npx envirollm benchmark-openai --url http://localhost:1234/v1 --model llama-3-8b
npx envirollm benchmark-openai --url http://localhost:8000/v1 --model meta-llama/Llama-2-7b-hf
npx envirollm benchmark-openai --url http://localhost:1234/v1 --model phi-3 --prompt "Custom prompt"
npx envirollm benchmark-openai --url http://localhost:1234/v1 --model llama-3-8b --api-key your-key

Data Management

npx envirollm clean # Remove all stored benchmark data

Process Monitoring

npx envirollm detect # List detected LLM processes
npx envirollm track --auto # Auto-detect and track LLM processes
npx envirollm track -p python # Track specific process by name
`

$3

Requirements:
- Ollama: Install Ollama and run ollama serve
- LM Studio/vLLM/Custom: API must be running on specified URL

Metrics Collected:
- Energy consumption (Wh)
- Tokens per second
- CPU/GPU/memory usage
- Quantization detection (Q4, Q8, FP16)
- Power draw (W)
- Response quality comparison

Data Storage:
All benchmark results are stored locally at ~/.envirollm/benchmarks.db. Your data never leaves your machine.

Web Interface Alternative

You can also run benchmarks using the web interface at envirollm.com/optimize after starting the monitoring service with npx envirollm start. The web UI provides:

- Visual model selection for Ollama, LM Studio, and custom APIs
- CSV export functionality for benchmark data
- Response comparison view to evaluate output quality
- Custom prompt configuration
- Same backend - results sync between CLI and web

Features

- Real-time Monitoring: CPU, GPU, memory, and power consumption
- Multi-Platform Benchmarking: Support for Ollama, LM Studio, vLLM, and OpenAI-compatible APIs
- Optimization Recommendations: System-specific suggestions for reducing energy usage
- Process Detection: Automatic identification of running LLM processes

How It Works

The CLI starts a local Python backend service that collects system metrics using psutil and pynvml`. The web dashboard at envirollm.com/dashboard automatically detects if you're running the local service and switches to display your real hardware metrics instead of demo data.

Benchmarking through the CLI or at envirollm.com/optimize runs inference requests against your local models while monitoring energy consumption, token generation speed, and resource usage in real-time.

License

MIT