How to Run DeepSeek Locally in 2026: Ollama, LM Studio & vLLM Setup Guide DeepSeek's models are MIT-licensed and open-source — meaning you can run them on your own hardware, no API key required, no monthly costs, data never leaves your machine. Here's a complete guide to running DeepSeek locally in 2026, covering three methods depending on your setup. Which Model Should You Run? Before picking a deployment method, pick a model size: Model Active Params VRAM (Q4 quant) Sweet Spot For R1 Distill 7B 7B ~5 GB RTX 3060, M2 Pro R1 Distill 14B 14B ~10 GB RTX 3090, M2 Max ← recommended R1 Distill 32B 32B ~22 GB RTX 4090, A100 40G V3 / V4 Full 671B–1.6T 400+ GB Multi-GPU server For most developers: R1 Distill 14B with Q4 quantization. Runs on a single RTX 3090 or Apple M2 Max, competitive reasoning quality, fast enough for interactive dev work. Method 1: Ollama (Easiest) Ollama handles download, quantization, and serving in one command. Works on macOS, Linux, and Windows.…