budget-gpu

Best budget GPU for local LLMs: how to compare without getting trapped

Learn how to compare VRAM, bandwidth, power, and real local price when choosing a GPU for local AI.

VRAM matters before FPS

For games, many comparisons focus on FPS. For local LLMs, the first question is how much VRAM the card has. A fast GPU with too little memory can lose to an older card that can simply load the model.

That does not mean performance is irrelevant. It means insufficient memory turns a theoretically good GPU into an offload-heavy experience.

Compare four things

The first point is VRAM. The second is memory bandwidth, which affects inference speed. The third is driver support on your operating system. The fourth is real local price, because used markets vary heavily by region.

Also check power draw. A cheap GPU that requires a new power supply may not be cheap in practice.

8GB, 12GB, or 16GB?

8GB can work for small models and some quantized 7B models. 12GB is a more comfortable entry point for serious local use. 16GB gives more room for context, larger models, and fewer compromises.

If you only want to learn, 8GB can be enough. If you want to use local AI every day, aim for 12GB or more when the budget allows.

Avoid buying from hype lists

There is no single best budget GPU for everyone. There is the best option in your market, for your target models, with your current power supply. Use CanIRunAI to filter models first, then compare local prices carefully.