GPU Comparison Tool

Side-by-side comparison of 15 GPUs for AI workloads. Filter by workload, price, or brand, then sort any column to find the right card for your budget. Each row links to our in-depth guide.

Prices and specs last checked September 15, 2026.

If you just want the answer

Best overall RTX 4090 24GB · ~$2,200

Handles 34B LLMs, SDXL, Flux. The default choice for most AI users.

 Best budget RTX 4060 Ti 16GB 16GB · ~$425

Cheapest new card with enough VRAM for 7B-13B LLMs and SDXL.

 Best used value RTX 3090 24GB · ~$820 used

Same 24GB as the 4090 for just over a third of the price. Inspect condition carefully.

 Maximum capability RTX 5090 32GB · ~$4,900

The fastest consumer card; 32GB reaches a 70B at q3, but 70B at Q4 (43GB) needs the 48GB A6000.

 Best for image gen RTX 4070 Ti Super 16GB · ~$800

Sweet spot for SDXL and Flux. 16GB covers every current workflow.

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15 of 15 GPUs match your filters

GPU VRAM Bandwidth Price (new) Price (used) 13B LLM SDXL TDP Best for
RTX 5090 NVIDIA 32GB GDDR7 1792 GB/s $4,900 — 55 tok/s 2.5s 575W 34B+ LLMs, flagship AI work, future-proofing
NVIDIA A6000 NVIDIA 48GB GDDR6 768 GB/s $3,500 $2,500 35 tok/s 5.5s 300W Single-card 70B LLM inference; ECC memory
RTX 4090 NVIDIA 24GB GDDR6X 1008 GB/s $2,200 — 40 tok/s 3.2s 450W Best overall for most AI users; 24GB covers 34B LLMs
RTX 5080 NVIDIA 16GB GDDR7 960 GB/s $1,400 — 32 tok/s 3.8s 360W 16GB + GDDR7 speed for 7B-13B; cheaper than 4090
RTX 5070 Ti NVIDIA 16GB GDDR7 896 GB/s $1,050 — 28 tok/s 4.3s 300W 16GB of GDDR7 just above the $1,000 line
RX 7900 XTX AMD 24GB GDDR6 960 GB/s $900 $850 25 tok/s 6s 355W 24GB AMD — Linux-first ROCm users only
RTX 5070 NVIDIA 12GB GDDR7 672 GB/s $875 — 20 tok/s 5.5s 250W Fast 7B models; 12GB limits 13B headroom
RTX 3090 (used) NVIDIA 24GB GDDR6X 936 GB/s — $820 35 tok/s 4.5s 350W Best VRAM/$ for AI — 24GB for about a third of the 4090
RTX 4070 Ti Super NVIDIA 16GB GDDR6X 672 GB/s $800 $737 24 tok/s 5s 285W Best sweet-spot GPU for AI under $1000
RTX 5060 Ti 16GB NVIDIA 16GB GDDR7 448 GB/s $630 — 22 tok/s 6.5s 180W The cheapest Blackwell card, and it still carries 16GB
RTX 4060 NVIDIA 8GB GDDR6 272 GB/s $479 — won't fit 10s 115W Entry-level; 8GB limits model sizes
RX 7800 XT AMD 16GB GDDR6 624 GB/s $450 $380 18 tok/s 8.5s 263W 16GB AMD — ROCm-comfortable users with Linux
RTX 4060 Ti 16GB NVIDIA 16GB GDDR6 288 GB/s $425 $350 18 tok/s 7.5s 165W Best VRAM per dollar of any new NVIDIA card; 16GB for 7B-13B LLMs
Intel Arc B580 INTEL 12GB GDDR6 456 GB/s $310 — 10 tok/s 11s 190W Experimental; the cheapest new 12GB card, but software gaps
RTX 3060 12GB (used) NVIDIA 12GB GDDR6 360 GB/s — $250 12 tok/s 9.5s 170W Cheapest way into real AI — 12GB at $250

Dive deeper

Already know roughly what you need? These guides go deep on specific buying questions:

Best GPU for AI The flagship buyer's guide Best Budget GPU for AI Under $500 picks Best Used GPU for AI 3090, 3080, 3060 12GB RTX 4090 vs 5090 Flagship comparison Best GPU for Stable Diffusion SDXL + Flux picks Workstation GPUs for AI A6000, RTX 6000 Ada

How to read this table

  • VRAM is the single most important spec for AI. It determines which models you can run at all. 16GB is the practical minimum for 2026 AI work.
  • Memory bandwidth (GB/s) drives LLM token generation speed — higher is faster at the same VRAM tier. GDDR7 > GDDR6X > GDDR6 at comparable cards.
  • 13B LLM (tok/s) estimates throughput running a 13B model at Q4_K_M quantization with short context (2-4K). Actual numbers vary with driver, context length, and framework.
  • SDXL (seconds) is time to generate one 1024×1024 image with a 25-step DPM++ 2M Karras sampler — standard A1111/ComfyUI benchmark config.
  • "Won't fit" means the target model at Q4 exceeds available VRAM with realistic KV cache / batch overhead — running it would OOM or fall back to painful CPU offload.
  • Price (used) reflects typical eBay / r/hardwareswap pricing at time of publish. Shift ±15% depending on seller condition and mining wear.

How to choose — the short version

VRAM first, then bandwidth, then price. Most AI headaches come from picking a GPU that can't load your target model. Match the model size you want to run (7B / 13B / 34B LLM, SDXL, Flux) to the minimum VRAM tier, then use bandwidth and price to decide within that tier. Used RTX 3090s routinely beat newer mid-range cards on VRAM-heavy workloads.

Performance numbers are representative ranges synthesized from manufacturer specifications, Tom's Hardware and TechPowerUp benchmarks, Puget Systems lab reports, and community benchmarks (LocalScore, r/StableDiffusion, r/LocalLLaMA). This is not first-party testing. See our methodology for how we evaluate GPUs and editorial policy for how we make recommendations.