CLI tool for monitoring local LLM resource usage
npm install envirollmbash
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.