Best GPU for Local AI
Interactive buying guide to find the perfect GPU for running Llama, Qwen, DeepSeek, and other open-source AI models locally. From budget-friendly options to workstation beasts.
Frequently asked
How much VRAM do I need?
- • Small models (7B): 8-12 GB at Q4_K_M
- • Medium models (13B-30B): 16-24 GB
- • Large models (70B): 40-48 GB
- • Very large (120B+): 80GB+ or multi-GPU
What's the best GPU for budget AI?
For entry-level local AI ($200-500), the RTX 3050-4060 class offers great value. At $500-1000, the RTX 4070 provides excellent performance for 30B+ models. Always check current pricing - GPU prices fluctuate significantly.
Can I use multiple GPUs?
Absolutely. vLLM supports tensor parallelism, llama.cpp distributes across GPUs, and Ollama auto-detects available GPUs. Multi-GPU setups unlock 70B-120B models.
What about AMD GPUs vs NVIDIA?
NVIDIA dominates due to mature CUDA ecosystem and broader model compatibility. AMD (ROCm) is improving but needs more technical setup. Intel Arc is an emerging budget option.
Is local AI cheaper than cloud APIs?
For high-volume inference, yes. A $500 GPU amortized over 3 years + electricity often beats cloud costs within 6-12 months. Use our cost vs cloud calculator to check your specific usage.