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Run LLMs, image generation, and ML models locally
Quick Answer: For most users, the RTX 4070 Ti Super 16GB ($750-$850) offers the best balance of VRAM, speed, and value. Budget builders should consider the RTX 3060 12GB ($250-$350), while professionals should look at the RTX 4090 24GB.
Choosing the right GPU for AI depends on your specific use case, budget, and the size of models you want to run. VRAM is the most critical factor - it determines which models fit in memory. We've tested dozens of GPUs and categorized them into three tiers based on real-world performance and value.
Compare all recommendations at a glance.
| GPU | VRAM | Price | Best For | |
|---|---|---|---|---|
RTX 3060 12GBBudget Pick | 12GB | $309.99 | Learning and experimentation, 7B-13B LLMs (Llama 3 8B, Mistral 7B) | Buy |
RTX 4070 Ti Super 16GBEditor's Choice | 16GB | $750-$850 | 32B models (DeepSeek, Qwen), Fast 7B-13B inference | Buy |
RTX 4090 24GBPerformance King | 24GB | $1,600-$2,000 | 70B LLMs (Llama 3 70B, Qwen 72B), Production inference | Buy |
Detailed breakdown of each GPU option with pros and limitations.
Best entry point for local AI. 12GB VRAM runs most 7B-13B models in Q4 quantization comfortably.
Best For
Limitations
Sweet spot for serious hobbyists. 16GB VRAM handles 32B models and runs SDXL at good speeds.
Best For
Limitations
The gold standard for local AI. 24GB VRAM runs 70B models in Q4, handles any consumer workload.
Best For