Best Budget GPUs for Local AI in 2025: VRAM Guide
Why VRAM Matters More Than Everything Else
When running LLMs locally, VRAM is the single most important spec. The entire model needs to fit in your GPU’s VRAM for fast inference. If it does not fit, you spill to system RAM and inference slows to a crawl.
How Much VRAM Do You Need?
| Model Size | Quantization | VRAM Required |
|---|---|---|
| 3B–4B | Q4_K_M | ~3GB |
| 7B–8B | Q4_K_M | ~5–6GB |
| 13B | Q4_K_M | ~9–10GB |
| 34B | Q4_K_M | ~22GB |
| 70B | Q4_K_M | ~45GB |
Best Budget GPUs
RTX 3060 12GB — Best Value ($220 used)
The RTX 3060 with 12GB VRAM is the home lab community’s favorite. You get more VRAM than the 3080 10GB, plenty for 13B models, and used prices are below $220. CUDA support means full compatibility with every local AI tool.
RTX 4060 8GB — Best New Under $300 ($299 new)
The RTX 4060 offers excellent performance per watt with DLSS 3 and AV1 encoding as bonuses. 8GB limits you to 7B models, but inference speed is excellent.
RX 6700 XT 12GB — Best AMD Pick ($200 used)
AMD’s ROCm support has matured significantly. The RX 6700 XT with 12GB at around $200 used is a solid Ollama-compatible pick, especially on Linux.
FAQ
Can I use an AMD GPU with Ollama? Yes, on Linux with ROCm. Windows AMD support is improving but still less mature than NVIDIA.
Is 8GB VRAM enough for local AI? Yes, for 7B models. The RTX 4060 or 3070 at 8GB handles everyday tasks well. Go 12GB if your budget allows.
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