Best GPU for Running AI Models Locally (2026)
Find the best GPU for running LLMs and AI models locally. We compare NVIDIA RTX 4060 Ti, 4070 Ti Super, 4090, and 5090 for local AI inference with real benchmarks.
Last updated: February 7, 2026
🎯 Why This Matters
Your GPU is the single most important component for running AI models locally. The GPU's VRAM (video memory) determines which models you can load, and its compute power determines how fast you get responses. A $400 GPU can run 7B models at 30+ tokens/sec — faster than most cloud APIs. Investing in the right GPU means you get instant, private AI without monthly fees.
🏆 Our Recommendations
Tested and ranked by real-world AI performance
NVIDIA RTX 4060 Ti 16GB
✅ Pros
- Best value for local AI in 2026
- 16GB VRAM handles most 7B-13B models
- Low power consumption (165W)
- Fits in any standard PC case
❌ Cons
- Can't run 30B+ models at full quality
- Slower bandwidth than higher-end cards
- No NVLink support for multi-GPU
NVIDIA RTX 4070 Ti Super 16GB
✅ Pros
- Nearly 2x faster than 4060 Ti for inference
- Excellent for Stable Diffusion XL
- Good balance of price and performance
❌ Cons
- Same 16GB VRAM as 4060 Ti — no model size advantage
- Higher power draw (285W)
- Diminishing returns vs 4060 Ti for pure LLM use
NVIDIA RTX 4090 24GB
✅ Pros
- 24GB VRAM unlocks 30B models
- Blazing fast inference
- 1 TB/s memory bandwidth
- Can handle SDXL with LoRA training
❌ Cons
- Expensive at $1,599
- 450W power draw — may need PSU upgrade
- Massive card — check case clearance
- Overkill for just 7B models
NVIDIA RTX 5090 32GB
✅ Pros
- 32GB VRAM for larger models
- PCIe 5.0 and massive bandwidth
- Next-gen CUDA cores
- Best single-GPU for local AI
❌ Cons
- $1,999 price tag
- 575W TDP — needs beefy PSU (1000W+)
- Limited availability in early 2026
- Massive physical size
💡 Prices may vary. Links may earn us a commission at no extra cost to you. We only recommend products we'd actually use.
🤖 Compatible Models
Models you can run with this hardware
❓ Frequently Asked Questions
Is NVIDIA or AMD better for local AI?
NVIDIA is strongly recommended for local AI. Nearly all LLM inference engines (llama.cpp, Ollama, vLLM) are optimized for CUDA. AMD ROCm support is improving but still has compatibility issues and fewer optimizations. Stick with NVIDIA unless you have a specific reason for AMD.
Can I use my existing gaming GPU for AI?
Yes! If you have an NVIDIA GPU with 8GB+ VRAM (RTX 3060 12GB, RTX 3070, etc.), you can run 7B models right now. The RTX 3060 12GB is actually a popular budget AI card. Just install Ollama and start running models.
Do I need a new GPU or will my old one work?
Any NVIDIA GPU from the GTX 1000 series onwards can technically run AI models, but you need enough VRAM. 8GB is the minimum for 7B models, 16GB for 13B, and 24GB+ for 30B. Older cards will be slower but functional.
Should I buy two cheaper GPUs or one expensive one?
One GPU is almost always better. Multi-GPU setups require splitting models across cards, which adds latency from inter-GPU communication. A single RTX 4090 (24GB) outperforms two RTX 4060 Ti cards (16GB each) for LLM inference in most scenarios.
Ready to build your AI setup?
Pick your hardware, install Ollama, and start running models in minutes.