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Learn about GPUs, VRAM requirements, and hardware specifications for running AI models locally.
Running AI models locally requires powerful hardware. The right GPU can make the difference between a smooth experience and frustrating performance.
Faster GPU = faster token generation and real-time responses
More VRAM = ability to run larger, more capable models
Higher precision (FP16) = better output quality
VRAM is the most important factor when choosing a GPU for AI. Different models require different amounts of VRAM to run effectively.
| Model Size | Minimum VRAM | Recommended VRAM | Example Models |
|---|---|---|---|
| 7B | 8GB | 12GB+ | Llama 3.2 7B, Mistral 7B, DeepSeek-Coder 7B |
| 13B-14B | 16GB | 24GB+ | Llama 3.2 14B, DeepSeek V3, Qwen 14B |
| 30B-70B | 32GB | 48GB+ | Llama 3.1 70B, GLM-4.7-flash 30B, Mixtral 8x7B |
Best for:
Small models (3-7B), basic coding assistants
Examples:
Best for:
Medium models (13-14B), advanced coding, image generation
Examples:
Best for:
Large models (30-70B), research, complex tasks
Examples: