What's the best GPU for local AI right now? The card with enough VRAM for your model, at the lowest price

The best GPU for local AI right now is the cheapest card with enough VRAM for the models you want to run. For most people in November 2026 that's a used RTX 3090: 24GB runs the 27–32B models most home users want, and at a $1,543 lowest eBay average on GPU Poet (checked Nov 12, 2026) it's the cheapest 24GB card with CUDA; a used RX 7900 XTX is a little cheaper ($1,415) if you accept AMD's software. Up to 14B models, a 12GB card is enough. Only buy the 32GB RTX 5090 if you need 30–35B models at long context, and only near its $1,999 list price; it was out of stock on NVIDIA's US marketplace on Nov 1, 2026. If you'd use the card a few hours a week, rent instead: a 24GB RTX 3090 was $0.09 an hour on Vast.ai when our tracker read prices on Nov 3, 2026.

How much VRAM do I need for local AI?

Enough to hold the model you want to run, because a model that doesn't fit in GPU memory spills into system RAM and slows to a crawl. So decide what you want to run before you look at a single price. Our rough map at Q4: 8GB handles small 7–8B models (Llama 3.1 8B, Qwen3 8B, a quantized gpt-oss-20B with offload); see the best local LLMs for 8GB. 12–16GB runs 14B models, with 16GB leaving room for long context (16GB picks). 24GB runs 27–32B models such as Qwen3.8 27B and Qwen3-32B. 32GB runs the 30–35B class at very long context. A dense 70B at Q4 needs about 40GB, so no single consumer card holds it. For your exact model, quantization and context length, use the VRAM calculator.

Which GPU should I buy at each budget?

Around $350, models up to 14B: a used RTX 3060 12GB is the safe CUDA pick at about $337 on eBay (GPU Poet, Nov 12, 2026). Intel's Arc B580 has the same 12GB and a $249 recommended price on Intel's own spec page, but it's good value only near that price: the only in-stock B580 in Newegg's search on Nov 1, 2026 was $699.

$850–$1,300, a new 16GB card: the RTX 5060 Ti 16GB ($869.99 and up) and RTX 5070 Ti ($1,299.99 and up) on NVIDIA's store on Nov 1 run 14B models with long context. A used 24GB card costs more again (about $1,543 for a 3090 on Nov 12) but runs a whole size class more, so price both before you decide.

About $1,550, the sweet spot for 24GB: a used RTX 3090. Its 24GB holds 27–32B models at Q4, and hardware-corner.net's 3090 benchmark page gives 52.1 tokens a second generating on a 14B model at 4-bit (Qwen3 14B; the page labels that figure 4K context in one place and 16K in another, and its table lists 70 t/s at 4K). Check the card's condition before you pay; our used RTX 3090 buying guide has the checks.

Should I buy a used RTX 3090 or an RTX 4090?

The 3090, unless you need the speed. Both have 24GB, so they run exactly the same models. The difference is speed: hardware-corner.net's relative index puts the 4090 at 151% of the 3090 for token generation, about 1.5x. At $3,133 against $1,543 (GPU Poet's lowest eBay averages, Nov 12, 2026), you pay about twice as much for that. If the gap between the two on the day you buy is much smaller, the 4090 gets more interesting. The full comparison is in RTX 4090 vs RTX 3090 for local AI.

Is the RTX 5090 worth it, and can you buy one?

Only if you need 32GB and find one near list. The RTX 5090 is the only consumer card with 32GB, which buys you the 30–35B class at very long context. It's worth it near its $1,999 list price, but on Nov 1, 2026 NVIDIA's US marketplace showed the Founders Edition at $1,999 and out of stock, with the RTX 5080 and 5070 Founders Editions out of stock too. NVIDIA's GeForce chief Jeff Fisher said in October that production has not been discontinued (PC Gamer, Oct 19, 2026); the January end-of-production dates circulating online are leaks. The full case is in is the RTX 5090 worth it for local AI.

Do I need 64GB of system RAM for local AI?

If you want big mixture-of-experts (MoE) models, yes. MoE models only use a fraction of their weights for each token. Offloading engines keep those active parts on the GPU and the rest in system RAM, so a 24GB card with 64GB of RAM can run models far bigger than 24GB. llama.cpp's MoE offload does this, and newer engines such as Strata, an open-source project started in September 2026, are built around it. One r/ollama user reported about 100 tokens/sec on a 125B MoE with a 3090 and 64GB of RAM; that's one person's report, not our test. If you're buying a 24GB card for big MoE models, budget for the RAM too: see how much RAM you need for local AI.

Should I rent a GPU instead of buying one?

If you'd use it a few hours a week, yes. When our tracker read prices at 12:20 UTC on Nov 3, 2026, a single 24GB RTX 3090 rented on Vast.ai from $0.09 an hour on-demand and a 32GB RTX 5090 from $0.46 an hour; on Runpod the same cards were $0.50 and $1.19 an hour. At $0.09 an hour, the roughly $1,543 a used 3090 costs would rent one for thousands of hours, though you'd pay for storage too and the cheapest offers come and go. Our rent vs buy break-even does the maths, and /cloud-gpu shows today's prices. In one r/LocalLLM thread, a well-upvoted reply made the same point: a $20 cloud subscription is a low bar to clear before buying hardware.

What if I need more than 32GB?

A single gaming card is the wrong answer here. Three routes:

• 32GB without a 5090. AMD's Radeon AI PRO R9700 is a 32GB RDNA 4 card AMD pitches for local AI inference, and two of them came up often in a recent r/LocalLLM thread on what to buy for up to $5,000. Expect more setup than with CUDA.
• Very large models in one box. Unified-memory machines share one big pool between CPU and GPU: an AMD Strix Halo box or an Apple Mac can hold models no gaming card can, at lower speed. We covered how much VRAM Strix Halo really gives you and Mac Studio vs an RTX Pro.
• A workstation budget. NVIDIA's RTX PRO 6000 has 96GB of GDDR7 with ECC and a 600W maximum, per NVIDIA's page on Nov 3, 2026. NVIDIA doesn't list a price there. It was the top suggestion in an r/LocalLLaMA thread on a $10–12K agentic-coding build. To try one first, Runpod listed it at $2.49 an hour in our Nov 3 reading.

Building the rest of the machine? See gaming laptop vs desktop for local AI if you're torn between portable and powerful, and check the VRAM calculator for your exact model and context before you buy anything.