How to Run a Local LLM on Your PC in 2026 (Complete Beginner Guide)

16GB RAM minimum for small models (Llama 3.3 8B, Phi-4). 32GB for mid-size (Llama 4 Scout, Qwen3 32B). A GPU with 12GB+ VRAM (RTX 4070 or better) for fast inference. Apple Silicon Macs work brilliantly thanks to unified memory.

Download Ollama (free, open source). Open terminal. Type ollama run llama3.3. That's it. You now have a local LLM chatting in your terminal.

Both wrap Ollama-style local models in a clean ChatGPT-like interface. LM Studio also runs an OpenAI-compatible API on your machine — point any tool that 'just supports OpenAI' at your local model.

Llama 4 Scout (8B effective), Qwen 3 14B Instruct, Phi-4, Gemma 3 12B. For coding specifically: Qwen 2.5 Coder 14B is the best small model we've tested.

Use AnythingLLM or Open WebUI. Drop in PDFs, get answers grounded in your files, fully offline.

on an RTX 4090, expect 60-80 tokens/sec on a 14B model. On an M3 Pro Mac, expect 25-40 tokens/sec. Fast enough for real work.

local models are 20-30% behind GPT-5 / Claude 4 on hard reasoning. For 80% of daily prompts you literally won't notice the difference. For complex coding or long analysis, cloud still wins.

Privacy. Cost. No internet required. Customization (you can fine-tune local models on your data). And the philosophical satisfaction of owning your own AI.