Eight common questions about buying a GPU for local AI, covering NVIDIA vs AMD, VRAM, and best value picks.
Q: NVIDIA or AMD for local AI?
NVIDIA is the safer choice in 2026. CUDA has broader software support, and almost every AI framework is tested on NVIDIA first. AMD's ROCm has improved significantly but still has compatibility gaps with some tools.
If you are on Linux and comfortable with occasional troubleshooting, AMD offers excellent value. See our AMD GPU guide and NVIDIA GPU guide.
Q: How much VRAM should I get?
8 GB is the minimum for a useful experience. 12–16 GB is the sweet spot for most users. 24 GB unlocks everything.
Refer to the VRAM requirements guide for a breakdown by task.
Q: What is the best value GPU for AI right now?
As of early 2026:
- Budget: RTX 4060 (8 GB) – ~$300, handles SD 1.5/SDXL and 7B LLMs
- Mid-range: RTX 4060 Ti 16GB – ~$450, the sweet spot for most AI tasks
- High-end: RTX 4090 (24 GB) – ~$1,600, runs everything including 70B LLMs
- AMD value: RX 7900 XT (20 GB) – ~$700, excellent VRAM per dollar (Linux only)
Q: Does system RAM matter for AI?
Yes. You need enough RAM to hold the model during loading and for CPU-offloaded layers. 16 GB is the minimum, 32 GB is recommended, 64 GB is ideal if you run multiple tools or large models.
Q: Can I use an Intel Arc GPU?
Intel Arc (A770, A750) has experimental support in some frameworks via SYCL. It is not yet reliable enough for daily use. We recommend NVIDIA or AMD for now.
Q: Should I buy used GPUs?
Used RTX 3090s (24 GB VRAM) are excellent value at $600–$800. The 24 GB of VRAM is more important than the newer architecture of a 4070. For AI, VRAM is king.
Check for mining damage: run a GPU stress test for 30 minutes and monitor for artifacts or thermal throttling.
Q: Do I need a workstation GPU (A100, H100)?
No. Consumer RTX cards are sufficient for inference and LoRA fine-tuning. Workstation GPUs are only necessary for full model training from scratch, which most users never do. The fine-tuning guide covers what consumer hardware can handle.
Q: What about eGPUs (external GPUs)?
eGPUs work but have a 20–30% performance penalty from Thunderbolt bandwidth limitations. They are a reasonable option if you have a laptop with no GPU slot. Thumper-Run's GPU detection supports eGPUs automatically.
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About the Author
Logan
Founder of Thumper-Run. Building local-first AI tools.
