AI Buyer's Guide · 51 GPUs measured first-party · Updated July 2026
The Best GPU for AI: Local LLMs, Image Generation & Video
AI pulls a GPU in three directions at once. Local LLM inference is bound by memory bandwidth and VRAM capacity. Image generation is bound by tensor compute. And video and image editing are bound by VRAM ceiling above all, miss it by a gigabyte and the model simply won't load. So we rented 51 GPUs and measured all three on the same 12-workload suite, from a 6GB RTX 2060 to a 288GB B300. This ranking rewards the cards that are genuinely good at AI, not the ones that are good at games.
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
The card I'd crown after measuring the whole board. 96GB of Blackwell with an AI Score of 53.1. It holds nearly everything in our suite, on the newest architecture, without stepping into datacenter chassis territory. The honest caveat: at $8,565 you should rent it first and buy it only when your workload never lets the meter stop.
- AI Score 53.1, highest workstation card we measured
- 96GB holds nearly the whole suite
- Blackwell architecture, the AI generation
- $8,565, rent-first territory
- Workstation pricing for consumer-adjacent silicon
- Overkill below 70B-class work
Best for: Serious AI builders whose workload justifies owning the top of the workstation stack. Everyone else should rent it by the hour.
NVIDIA GeForce RTX 5090
The best consumer AI card, full stop. 268.1 tok/s measured on Llama 3.1 8B, 32GB clears the FLUX.1-dev floor, and on pure text generation it gets surprisingly close to datacenter silicon several times its price. If you want one card in a desktop that does local AI properly, this is it, AI Score 22.3.
- 268.1 tok/s on Llama 3.1 8B (measured)
- 32GB, the real minimum for serious local AI now
- Shockingly close to a B300 on text generation
- Still can't hold a 70B (~46GB needed)
- 575W under sustained AI load
- $1,999, renting covers a lot of hours
Best for: Anyone building one machine that does serious local AI without workstation prices.
NVIDIA GeForce RTX 3090
The used-market champion, and the numbers explain why. 144.9 tok/s on Llama 3.1 8B is 54% of a 5090's speed for a fraction of the money, because token generation is bandwidth-bound and the 3090's 936 GB/s still holds up. 24GB runs 32B models comfortably. One honest update: used 3090s were ~$700 when this was the no-brainer. They're pushing $1,100 now, and at that price you should ask whether renting newer Blackwell silicon at ~$1/hour serves you better. It stays on this list because 24GB in a gaming-capable card is still unmatched as a combo.
- 24GB for the lowest price on the market
- 144.9 tok/s on Llama 3.1 8B (measured)
- Runs Qwen3 32B at 37.95 tok/s
- 3.73 it/s on SDXL. Ampere is slow at diffusion
- FLUX.1-dev won't fit at BF16
- Used-only; no warranty
Best for: Budget-minded local-LLM builders who want 24GB cheap and don't care about image speed.
NVIDIA H200
The sweet middle ground of the rental market. 141GB holds everything in our suite including the 70B class, and it prices well under the B300 while giving up less than the spec gap suggests, the B300's edge is really in image and video, not text. AI Score 65.0, measured first-party.
- 141GB, nothing in our suite won't fit
- Better value per rented hour than the B300 for LLM work
- All 12 workloads measured first-party
- Not a purchase, rental only for normal humans
- B300 pulls ahead on heavy image/video
- Availability varies by provider
Best for: 70B-class inference and big fine-tunes where you want datacenter capacity without paying the flagship hourly rate.
NVIDIA GeForce RTX 4060 Ti 16GB
The 16GB SKU specifically, and for the money it's pretty nice. That 16GB runs Z-Image and LTX video, workloads that flat-out refuse 12GB cards costing more. It's not fast (AI Score 3.2), but it's the cheapest ticket to actually running the interesting models rather than being told no.
- 16GB runs models that gate 12GB cards
- ~$499, the budget VRAM play
- Measured first-party on all fitting workloads
- Slow relative to everything above it
- Make sure you buy the 16GB SKU, not the 8GB
- Bandwidth limits it on bigger LLMs
Best for: Budget builders who care more about what fits than how fast it runs.
AMD Radeon RX 7900 XTX
AMD's flagship is genuinely competitive where LLMs are concerned, an estimated 127.9 tok/s on Llama 3.1 8B with 24GB: but our harness is CUDA-only, so this is an anchored estimate, not a measurement, and we say so.
- 24GB at $999
- Estimated 127.9 tok/s on Llama 3.1 8B
- Strong bandwidth for LLM work
- Estimated, not measured, no ROCm harness yet
- 2.67 it/s estimated on SDXL, well behind NVIDIA
- Ecosystem friction on diffusion tooling
Best for: LLM-first users open to AMD who value VRAM per dollar over image-generation speed.
The single biggest mistake people make buying a GPU for AI is shopping by gaming benchmarks. AI doesn't work like games. Local LLM inference, running a chatbot or coding assistant on your own machine, is limited by memory bandwidth and VRAM capacity, not raw compute. In our measurements a used RTX 3090 hits 144.9 tok/s on Llama 3.1 8B, within a third of an RTX 5090's 268.1, for a fraction of the price, because both are bandwidth-bound long before they're compute-bound. Image generation flips the script. SDXL, Z-Image and FLUX are tensor-compute bound, which is where newer silicon and NVIDIA's CUDA stack pull clearly ahead: the 5090 does 10.54 it/s on SDXL against the 3090's 3.73, nearly 3x, on the same workload where their LLM gap was small. But the number that decides most purchases isn't speed at all. It's the VRAM cliff. Llama 3.3 70B needs roughly 42GB at Q4. So it runs on a 48GB RTX 6000 Ada at 18.4 tok/s, and it does not run at all on a 4090, a 5090, or a 3090. No driver update fixes that. Qwen-Image-Edit needs ~42GB and FLUX.1-dev needs ~26GB, which is why a 24GB 4090 turns both down while a 32GB 5090 handles FLUX fine. When we tested every card on every workload, the cards that won weren't the fastest. They were the ones that could actually hold the model. That's why our AI Score treats a won't-fit result as a zero rather than hiding it. A card that can't run the job doesn't get partial credit for being quick at the jobs it can run.
Our verdict
Buy on VRAM first, bandwidth second, brand third, and be honest about the rent option at every tier. For most people the RTX 5090's 32GB is the real minimum for serious local AI now; 16GB and 24GB are effectively the same tier once you account for context-window headroom, so on a tight budget go 16GB and rent for the big jobs. Full disclosure of my own bias: having measured all of these, the card I'd buy with my own money is the RTX 4080 16GB, the best gaming-plus-light-AI value per dollar on the board, and I'd rent everything above the consumer tier. If your work needs 70B models, image editing, or video, no consumer card will do it: rent an H200 by the hour instead of buying anything at all.
FAQ
- How much VRAM do I need for AI?
- It depends entirely on the model, and the cliff is sharp. From our measurements: Llama 3.1 8B at Q4 needs ~6GB, Qwen3 32B needs ~20GB, and Llama 3.3 70B needs ~42GB. On the image side SDXL needs ~8GB, FLUX.1-dev needs ~26GB, and Qwen-Image-Edit needs ~42GB. A model that doesn't fit doesn't run slowly, it doesn't run. That's why we publish won't-fit results as data.
- Is AMD good for AI?
- For LLM inference AMD is more competitive than its reputation suggests, because token generation is bandwidth-bound and the RX 7900 XTX has plenty. For diffusion and video the CUDA ecosystem is still a real advantage. We're honest about our limits here: our benchmark harness is CUDA-only, so every AMD number on this site is an anchored estimate against measured NVIDIA cards, never a first-party measurement.
- Is a 5090 worth it over a 4090 for AI?
- On our numbers, yes, more than the gaming gap suggests. The 5090 posts 268.1 tok/s on Llama 3.1 8B against the 4090's 171.3 (57% faster) and 10.54 vs 8.14 it/s on SDXL. More importantly its 32GB clears the ~26GB FLUX.1-dev floor that the 4090's 24GB misses, so it runs models the 4090 refuses outright.
- Can any consumer GPU run Llama 3.3 70B?
- No. At Q4 it needs roughly 42GB and the largest consumer card is the 5090 at 32GB. We measured 70B running on 48GB workstation cards (RTX 6000 Ada at 18.4 tok/s) and datacenter cards (H100 at 41.0 tok/s). For a single consumer GPU, 70B is out of reach, rent instead.
- I have $800. What should I buy for local LLMs today?
- Honestly? Nothing, rent instead. $800 buys 100+ hours on a rented B300 with 288GB of VRAM, one of the fastest single-GPU setups that exists, versus a mid-range consumer card that gates half the models on this page. Blackwell is the best generation for AI, and the rental market is currently discounted while providers fight for customers. Buy a card once you know exactly which models you run every day; rent while you're figuring that out. Our full provider breakdown is in the RunPod vs Vast.ai vs Modal guide.
- Does more VRAM always make a card better for LLMs?
- No. And this is the trap. Text and code generation hit diminishing returns on VRAM; the extreme capacities pay off for image, video and 3D generation, which are far more VRAM-hungry. It's also the combination that matters: a card (or CPU build) that fits a big model but generates single-digit tokens per second isn't usable as a real tool. Bolting more memory onto old silicon doesn't help, because the compute isn't there to run the bigger model at speed. Capacity decides what runs; bandwidth and compute decide whether you'd want to use it.
How we test
Every ranking on GPU Battle is built on our own benchmark dataset, not vendor marketing. Gaming cards are measured across the Core-9 suite at 1440p; VR cards across six real VR titles at full per-eye resolution. AI cards are measured first-party across 12 workloads: the Qwen3-4B to Llama-3.3-70B LLM ladder (llama.cpp, Q4_K_M), SDXL / Z-Image / FLUX.1-dev generation, FLUX Kontext and Qwen-Image-Edit editing, and LTX / Wan video, all at pinned versions with under 0.5% run-to-run variance, with peak VRAM, power draw and temperature logged per run. We rented and measured 51 GPUs ourselves; the rest are estimates anchored to those measurements and clearly labelled as such. Where a model exceeds a card's VRAM we publish a hard "won't fit" result rather than quietly dropping to a smaller quant, a card that can't run a model scores zero on it. Numbers on this page are pulled live from those datasets, so the table and per-card stats stay in sync with our benchmarks as they're updated.