Guide · Local AI

Best GPU for local AI under $500

Updated October 2026. I haven't benchmarked every card here. The picks come from published specs and speeds that owners report, and I point out the trade-offs.

As an Amazon Associate I earn from qualifying purchases. Buying through these links supports the site at no extra cost to you. Full disclosure

For running language models at home with Ollama or LM Studio, one spec decides almost everything: VRAM. If the model fits in the card's memory, it runs fast. If it spills into system RAM, it slows down by 5 to 20 times. CUDA core counts barely matter for this, and memory bandwidth comes a distant second. So under $500, the job is simple: get the most usable VRAM you can.

Prices moved a lot this year. Two cards I used to recommend here, the RTX 5060 Ti and a used RTX 3090, now cost well over $500, so they've moved to their own section below. If you already own a PC, run it through the Server Calculator first. You might not need a new card at all.

Words you'll see in this guide
  • VRAM: the memory on your graphics card. AI models load into it, so more VRAM means you can run bigger models.
  • Parameters (7B, 14B, 30B): a rough measure of a model's size. "8B" means 8 billion. Bigger models are usually smarter and need more memory.
  • Quantization: shrinking a model by storing its numbers less precisely, so it fits in less memory with a small drop in quality. Think of saving a photo as a smaller JPEG. "4-bit" (Q4) is the common setting.
  • Tokens per second: how fast a model writes. A token is roughly three-quarters of a word.
  • CUDA: Nvidia's software for running AI on its graphics cards. Most local AI tools support it first and best.

See the full glossary

How much VRAM you need

At the usual 4-bit setting, plan on about 0.6 GB of VRAM per billion parameters, plus a couple of GB for overhead. That works out to:

Pick the card for the model size you'll actually use day to day.

The two picks that are actually under $500

1
RTX 3060 graphics card
Best value

RTX 3060 12 GB (used)

Still the cheapest way into 12 GB of Nvidia VRAM. A used RTX 3060 12 GB fits every 7B and 8B model and most 13B and 14B models at 4-bit. Community benchmarks put it at roughly 40 to 55 tokens per second on an 8B model, which is faster than you can read. CUDA support is mature, so Ollama and LM Studio work without fiddling. It's also the card I picked for my own AI rig.

Not for you if: you want 30B models. 12 GB isn't enough, and no amount of tuning changes that.

Check price on Amazon
2
Intel Arc A770 graphics card
Cheapest 16 GB

Intel Arc A770 16 GB

The Intel Arc A770 16 GB is now the only new 16 GB card I can find under $500. Ollama and LM Studio both run on it. Intel's software is less polished than Nvidia's CUDA, so expect the occasional setup problem and fewer guides when something breaks.

Check before you buy: the A770 also comes in an 8 GB version, and listings don't always make that obvious. Make sure the one you order says 16 GB.

Check price on Amazon

Worth it if your budget stretches

These two used to sit in the top picks. They're still good cards for local AI, but neither is anywhere near $500 right now.

Side by side

CardVRAMRuns comfortablyUnder $500?
RTX 3060 (used)12 GB7B to 14B modelsYes
Intel Arc A77016 GB14B modelsYes
RTX 5060 Ti16 GB14B with headroomNot right now
RTX 3090 (used)24 GB30B-class, or 70B with twoNo

Price ranges above come from retailer and eBay trackers in October 2026, not from Amazon. They change weekly, so check the current price before you buy.

Buying a whole machine instead of a card? Look at a Mac. Apple Silicon shares one pool of memory between the processor and graphics, so a Mac with 32 GB or 64 GB can run models that a similarly priced PC without a graphics card can't. It's also nearly silent. The Server Calculator covers Macs too.

What I'd buy

Whatever you pick, buy for VRAM first. Once you have the card, the next step is building the rest of the home AI server around it.

Affiliate disclosure: Some links on this page are affiliate links. If you buy through them, I may earn a small commission at no extra cost to you. As an Amazon Associate I earn from qualifying purchases. No manufacturer paid for placement, and nothing here is sponsored.