Budget local AI rig: 16 GB GPU build
A 16 GB card is the cheapest way to run 14B-class models fully on the GPU, plus gpt-oss-20b at native precision. See exactly what fits on our 16 GB tier page.
We don't show prices here because we only print prices we checked the same day, and Amazon prices change often. Click through to see today's price.
Parts list
| Part | Pick | Why | Link |
|---|---|---|---|
| GPU | GeForce RTX 5060 Ti 16GB | 16 GB GDDR7, 128-bit bus, 180 W total graphics power (NVIDIA). Make sure the listing says 16 GB: an 8 GB version also exists. | Check price on Amazon |
| GPU (AMD option) | Radeon RX 9060 XT 16GB | 16 GB GDDR6, 160 W typical board power (AMD). Check your app supports AMD first (llama.cpp and Ollama do). | Check price on Amazon |
| System RAM | 32 GB DDR5 (2×16 GB) | Room to load models and to offload a few layers to the CPU when a model is slightly too big. | Check price on Amazon |
| Storage | 1 TB NVMe SSD | Model files are large; a 14B model at Q4 is several GB and you'll keep more than one. | Check price on Amazon |
| Power supply | Quality ATX PSU sized to NVIDIA's/AMD's recommended system power | Check the "required system power" on the card's spec page and buy at or above it. | Check price on Amazon |
| CPU + motherboard | Any current 6-core desktop CPU with a matching board | The GPU does the heavy lifting when the model fits in VRAM. | Check price on Amazon |
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Next steps
- Compare every 16 GB card in our 16 GB buying guide.
- Check your exact model in the VRAM calculator.
- Step up: 24 GB rig · 32 GB rig.
- Only need it a few hours a month? Renting a GPU may be cheaper.