Guide 11 min 2025-02-20
Running Open-Source LLMs Locally: A Complete Guide
Everything you need to know about running LLaMA, Mistral, and other open-source models on your own hardware.
Running AI models locally gives you privacy, zero API costs, and full control. Here's how to get started with the best open-source options.
Why Run Models Locally?
- **Privacy** — Your data never leaves your machine
- **Cost** — No per-token charges
- **Speed** — No network latency
- **Customization** — Fine-tune for your specific needs
Getting Started with Ollama
The easiest way to run local models:
bash
# Pull and run a model ollama run llama3.2
# Use the OpenAI-compatible API curl http://localhost:11434/v1/chat/completions \ -d '{"model": "llama3.2", "messages": [{"role": "user", "content": "Hello!"}]}' ```
Recommended Models by Use Case
| Model | Size | Best For |
|---|---|---|
| LLaMA 3.2 3B | 2GB | General chat, fast responses |
| Mistral 7B | 4GB | Balanced quality/speed |
| CodeLlama 13B | 8GB | Code generation |
| Mixtral 8x7B | 26GB | Best open-source quality |
Hardware Requirements
- **Minimum**: 8GB RAM, any modern CPU (runs 3-7B models)
- **Recommended**: 16GB RAM, GPU with 8GB VRAM
- **Ideal**: 32GB RAM, GPU with 24GB VRAM (runs 70B models)
See our open-source tools page for more details.
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