Video cards
Best GPUs for local AI: what to buy by memory and budget
The best graphics cards for running local AI, chosen by usable VRAM, current tracked prices, software support, and the point where a desktop AI system makes more sense.
By David L. Published
The short answer is simple: buy enough VRAM first, then worry about speed. A fast card that cannot hold your model is the wrong card.
This is buying advice based on current specifications, prices, and software support, not a hands-on benchmark review. Prices move quickly, so follow the linked card pages for the latest verified listings.
My picks
| What you need | My pick | Why |
|---|---|---|
| Learn local AI without spending a fortune | Intel Arc B580 | 12 GB gives you more room than the usual 8 GB starter card. Buy it only after checking support for your exact apps. |
| Best practical new build | RTX 5070 Ti | 16 GB and NVIDIA’s software ecosystem make it the least complicated serious starting point. |
| More memory without workstation pricing | Radeon RX 7900 XTX | 24 GB is useful when capacity matters more than having the easiest software path. |
| Strongest consumer option | RTX 5090 | 32 GB is the most memory in a current GeForce card, but only buy when the street price makes sense. |
| Specialized 96 GB workstation | RTX PRO 6000 Blackwell | Huge ECC memory capacity for work that can justify a five-figure GPU. This is not a normal enthusiast upgrade. |
Start at 16 GB if you are buying new
Eight gigabytes still works for smaller quantized language models, image generation experiments, and learning the software. I would not build a new local-AI machine around it unless the budget is hard-capped.
Sixteen gigabytes is where the machine starts to feel useful instead of educational. The RTX 5070 Ti is my straightforward pick here because NVIDIA support is still the path of least resistance across local model tools. The RX 9060 XT 16 GB can cost less, but that savings only matters if the software you plan to use supports AMD well.
At 24 to 32 GB, the card becomes a real AI tool
The RX 7900 XTX has 24 GB. The RTX 5090 has 32 GB. That extra capacity gives larger models, longer context, and heavier image or video workflows more breathing room.
I would choose the RTX 5090 when broad compatibility matters and the current price is defensible. NVIDIA lists a $1,999 starting price, but the tracked market has often been much higher. Do not treat a bad street price as the cost of doing AI. Wait, buy used carefully, or compare a different class of machine.
Do not jump to a professional GPU without doing the math
The 96 GB RTX PRO 6000 Blackwell is attractive on a spec sheet. It is also a specialized purchase. You are paying for memory capacity, ECC, professional positioning, and the ability to keep a conventional workstation architecture.
If your workload needs more than 32 GB but does not require a standard PCIe GPU, look at desktop AI systems with 128 GB of unified memory. They trade some flexibility and raw discrete-GPU behavior for a much larger shared memory pool in a compact box.
What I would buy
For a new machine, I would start with 16 GB and NVIDIA unless I had a clear reason not to. I would move to 24 or 32 GB only because a real model or workflow needed it. I would not buy a five-figure professional card before comparing a 128 GB desktop AI appliance and cloud compute.
That is the boring answer. It is also the answer least likely to leave an expensive card sitting idle.
Sources and limits
- Current prices and card specifications come from the site’s tracked GPU catalog and are checked against exact marketplace listings.
- NVIDIA’s GeForce RTX 5090 page documents 32 GB GDDR7 memory and a $1,999 starting price.
- This guide does not claim measured tokens per second, thermals, noise, or application benchmarks. Those require controlled hands-on testing.
Questions buyers ask
- How much VRAM do I need for local AI?
- Eight gigabytes is enough to learn with smaller quantized models, but 16 GB is a better floor for a new local-AI machine. Move to 24 or 32 GB when model size and context are the priority, and consider unified-memory desktop AI systems when your work regularly exceeds 32 GB.
- Is the RTX 5090 the best GPU for local AI?
- It is the most capable consumer GPU in this site's current catalog, with 32 GB of memory and broad NVIDIA software support. It is not automatically the best purchase because current street prices can sit far above its $1,999 launch MSRP.
- Should I buy AMD or NVIDIA for local AI?
- NVIDIA remains the safer default when you want the broadest compatibility and the least setup friction. AMD can offer more memory for the money, but you should verify that your exact tools and operating system support the card before buying.