GPUs are used for AI because neural networks need billions of parallel matrix calculations, which GPU cores and Tensor Cores process far faster than a CPU. You can run AI on a local GPU in your PC or rent a cloud GPU by the hour, from about $0.60 per hour for an RTX 4090 to $2.27 per hour for an H100 in October 2026.
Why are GPUs used for AI?
GPUs are used for AI for 3 reasons:
- Massive parallelism: thousands of cores run matrix math at the same time.
- Tensor Cores and AI accelerators for low-precision formats such as FP8 and FP4.
- High memory bandwidth VRAM for large models.
Read CPU vs GPU and NPU vs GPU for how other processors compare.
How much VRAM do you need for AI?
The VRAM you need for AI depends on model size:
| Workload | Recommended VRAM |
|---|---|
| Image generation (Stable Diffusion, Flux) | 12–16 GB |
| Small LLMs (7–8B parameters) | 8–16 GB |
| Mid-size LLMs (13–34B) | 24–48 GB |
| Training and large models | 80 GB+ data center GPUs |
How do you check if your GPU works with PyTorch?
You check if your GPU works with PyTorch by running torch.cuda.is_available(). The commands and fixes are in PyTorch: how to check if GPU is available.
Should you rent or buy a GPU for AI?
You should rent a GPU for occasional or large jobs and buy one for daily local use. Compare providers and hourly prices in GPU rental, and purchase prices in the GPU price tracker.