VRAMfit guide · updated 2026-06-28
Best budget GPU for running AI locally
In short: the best budget GPU for local AI is the most VRAM you can afford in the 12-16 GB range. A 16 GB card runs useful ~14B models comfortably and a long list of smaller models fast, which covers chat, coding, and RAG. Below 8 GB you are limited to small models, so 12-16 GB is the value sweet spot.
Cheapest cards that run useful models
Retail 12-16 GB cards with the biggest model each runs comfortably at Q4_K_M / 8,192 context, and how many catalog models fit comfortably:
| Card | VRAM | Biggest comfortable model | Est. speed | Models that fit |
|---|---|---|---|---|
| AMD Radeon RX 9070 XT | 16 GB | gpt-oss:20b (20B) | ~31 tok/s | 278 |
| NVIDIA GeForce RTX 5060 Ti 16GB | 16 GB | gpt-oss:20b (20B) | ~22 tok/s | 278 |
| NVIDIA GeForce RTX 5070 | 12 GB | deepseek-r1:14b (14B) | ~47 tok/s | 263 |
| Intel Arc B580 | 12 GB | deepseek-r1:14b (14B) | ~32 tok/s | 263 |
| AMD Radeon RX 7700 XT | 12 GB | deepseek-r1:14b (14B) | ~30 tok/s | 263 |
Why 16 GB is the budget sweet spot
16 GB holds a 14B model with context to spare and runs 7-9B models very fast. It is the cheapest tier that comfortably covers the models most people actually use day to day, which is why it beats stretching to 24 GB on a budget.
The budget pick
The NVIDIA GeForce RTX 5060 Ti 16GB runs 278 catalog models comfortably - up to gpt-oss:20b at about 22 tok/s. See the full fit list on the fit board.
Frequently asked questions
How much VRAM do I need on a budget?
Aim for 16 GB. It runs ~14B models comfortably and many smaller models fast - the range most local-AI users actually need - without paying for a 24 GB card.
Can a cheap GPU run AI locally?
Yes. A 12-16 GB card runs 7-14B models well via Ollama/llama.cpp. You only need expensive cards for 30B+ models.
Is a used GPU a good budget buy?
Often the best value - a used 16 GB card runs the same models a new one does. Match the VRAM to the model size you want; the fit math is identical.