Best GPU for Stable Diffusion in 2026

Updated October 5, 2026. All prices are launch MSRPs from our GPU database; we do not track street prices.

How fast a card generates images depends on its memory bandwidth, and the benchmarks below make that visible.

For Stable Diffusion and SDXL image generation in 2026, the NVIDIA GeForce RTX 5090 is the best GPU overall because its 32 GB of GDDR7 and 1,792 GB/s of bandwidth produce the highest measured generation throughput of any consumer card. The best budget pick is the GeForce RTX 4060 Ti 16GB at a $499 MSRP, and the best value on the used market is the RTX 3090 with 24 GB. This guide ranks the options using benchmark data from our June 2025 database records.

How fast is each GPU at image generation?

The table below compares SDXL Turbo generation speed in ComfyUI with the specification data behind each card. Images per minute were recorded in June 2025; the RX 7900 XTX result was measured on ROCm.

GPU	VRAM	Bandwidth	TDP	MSRP	SDXL Turbo (img/min)	Price check
GeForce RTX 5090	32 GB	1,792 GB/s	575 W	$1,999	120	Check price on Amazon
GeForce RTX 4090	24 GB	1,008 GB/s	450 W	$1,599	80	Check price on Amazon
GeForce RTX 5080	16 GB	960 GB/s	360 W	$999	65	Check price on Amazon
GeForce RTX 4080 SUPER	16 GB	736 GB/s	320 W	$999	58	Check price on Amazon
GeForce RTX 3090	24 GB	936 GB/s	350 W	$1,499	45	Check price on Amazon
GeForce RTX 4060 Ti 16GB	16 GB	288 GB/s	160 W	$499	28	Check price on Amazon
Radeon RX 7900 XTX	24 GB	960 GB/s	355 W	$999	40	Check price on Amazon

Benchmark attribution: Tom's Hardware measured the RTX 5090, RTX 4090, and RX 7900 XTX; TechPowerUp measured the RTX 5080, RTX 4080 SUPER, and RTX 4060 Ti 16GB; Puget Systems measured the RTX 3090. Bandwidth sets the ceiling on diffusion throughput but does not order this table on its own, so we rank on the measured column and read the specification columns as context.

Two rows make that distinction concrete. The RTX 4080 SUPER carries 736 GB/s against the RTX 3090's 936 GB/s, yet generates 58 images per minute to the 3090's 45, so a 200 GB/s bandwidth deficit still loses to an older card on throughput. The Radeon RX 7900 XTX has exactly the same 960 GB/s as the RTX 5080 and produces 40 images per minute against 65, a gap no pair of datasheets explains. Memory bandwidth sets what a card can pull per denoising step; architecture, software stack, and the test harness decide where it actually lands underneath that ceiling. That is why we do not rank these cards by their bandwidth column.

Our database flags measured rows separately from estimated ones, and the difference is visible in this section. Every row in the table above is a measured result: SDXL Turbo at FP16 in ComfyUI, all tested on the same date, 1 June 2025. The one SDXL Turbo figure we hold that carries the estimate flag is the Arc B580's 18 images per minute, and we keep it out of the ranked table for that reason rather than presenting a sizing estimate alongside measured numbers.

Our database also holds SDXL Turbo rows above the consumer ceiling, which are useful as a boundary on what extra VRAM buys. The 48 GB RTX 6000 Ada manages 75 images per minute on 960 GB/s, slightly behind the 24 GB RTX 4090's 80 on 1,008 GB/s, and the 48 GB RTX A6000 manages 35 on 768 GB/s. The H100 SXM sits well above all of them at 200 images per minute, but that figure comes from NVIDIA rather than an independent lab, so we do not fold it into a consumer ranking. Read together these rows say capacity and bandwidth bound the workload rather than set the pace: double the RTX 4090's VRAM bought the Ada card nothing on throughput.

Which GPU is best for high-volume image generation?

Best overall: GeForce RTX 5090. According to Tom's Hardware, it generates SDXL Turbo images at 120 images per minute in ComfyUI, the highest figure in our benchmark database.

The specification profile explains the result: 32 GB of GDDR7 on a 512-bit bus delivers 1,792 GB/s of bandwidth, and 21,760 CUDA cores provide the compute behind each denoising step. The 32 GB pool also matters for workload headroom, because batch generation and additional models such as ControlNet all consume VRAM on top of the base checkpoint. The costs are a $1,999 MSRP and a 575 W TDP that requires a well-cooled system. Buyers who generate professionally will notice the throughput difference daily; casual users will not.

Check the GeForce RTX 5090 on Amazon →

Which GPU is best for Stable Diffusion on a budget?

Best budget pick: GeForce RTX 4060 Ti 16GB. It is the cheapest new NVIDIA card in our database with 16 GB of VRAM, at a $499 MSRP with a modest 160 W TDP.

According to TechPowerUp, the RTX 4060 Ti 16GB generates SDXL Turbo images at 28 images per minute, which is the slowest new-card figure in the table. The case for it is capacity, not speed: 16 GB of VRAM covers SDXL and SD3.5-class checkpoints with room for batching, and the card runs in ordinary desktops without power or cooling upgrades. Buyers whose alternative is a 12 GB card will feel the extra memory long before they miss the speed.

Check the GeForce RTX 4060 Ti 16GB on Amazon →

Which used GPUs are worth buying for diffusion?

Best used value: GeForce RTX 3090. It matches the RTX 4090's 24 GB of VRAM at a much lower used-market price, and Puget Systems measured it at 45 SDXL Turbo images per minute.

The RTX 4090 is the stronger used buy when budget allows: Tom's Hardware measured 80 images per minute from its 1,008 GB/s of bandwidth, the highest figure among 24 GB cards in our table. Between them sits the RTX 4080 SUPER at $999 MSRP with 58 images per minute per TechPowerUp, and the RTX 5080 at the same launch price with 65 images per minute on its 960 GB/s of GDDR7. All four cards appear in the price-check column of the table above, so current retail listings are one click away.

Check the GeForce RTX 4090 on Amazon →

How much VRAM does Stable Diffusion need?

Most Stable Diffusion workflows run on 16 GB cards, while the largest diffusion checkpoints benefit from 24 GB or more. Capacity buys workflow freedom rather than raw speed.

The evidence in our database is benchmark-based rather than formula-based. Every 16 GB card in the table generates SDXL Turbo images at full throughput, and TechPowerUp's estimate for the 12 GB Arc B580 comes in at 18 images per minute, so even 12 GB handles SDXL-class models. The 24 GB tier earns its keep with larger checkpoints and heavier batch workflows: the RX 7900 XTX's 40 images per minute on ROCm and the RTX 3090's 45 both leave VRAM to spare. Cards in the 12 GB class, such as the RTX 3060 that anchored budget builds for years, remain usable for SDXL but leave little headroom for batching.

A second lab run in our database separates capacity from speed more directly. StorageReview measured the GeForce RTX 5090, RTX 4090, RTX 5080, and RTX 6000 Ada in the UL Procyon AI Image Generation suite on a single lab platform (ThreadRipper 7980X, driver 571.86) on 29 January 2025, reporting seconds per image. On Stable Diffusion XL those four cards took 5.223, 7.461, 8.808, and 12.323 seconds per image respectively, which places the 48 GB RTX 6000 Ada last, behind the 16 GB RTX 5080. Extra VRAM does not make diffusion denoise faster; it changes what you can keep resident while it runs.

That same run shows why capacity still matters. On Stable Diffusion 1.5 the four cards took 0.763, 1.188, 1.344, and 1.477 seconds per image, so SDXL costs each card roughly six to eight times the time of its SD 1.5 pass. Per-step work rises with the checkpoint, and the checkpoint that outgrows a smaller card is what pushes you up a tier. That is the practical form of the rule: buy VRAM for the model and batch size you intend to run, not for throughput you will never see.

The newest open models make that concrete, though our figures for them are directional rather than lab-verified. Our FLUX.1-dev rows record roughly 26 GB of VRAM in use on the RTX 5090 and, on the RTX 4090, note that memory-efficient attention (xFormers or SDPA) is required to fit within 24 GB. Both rows carry our estimate flag from a tier-3 source, so treat them as sizing guidance rather than measured throughput. They do explain the shape of the ladder, and why buyers chasing the largest open checkpoints should read our Flux.1 GPU guide next. If you already know which card and which checkpoint you want, our VRAM calculator sizes that pairing before you spend.

Can AMD or Intel GPUs run Stable Diffusion?

Yes, with software caveats. The Radeon RX 7900 XTX runs SDXL Turbo at 40 images per minute per Tom's Hardware ROCm testing, while Intel's Arc B580 manages an estimated 18 images per minute per TechPowerUp.

The specification comparison favors AMD on paper at the same $999 MSRP as the RTX 4080 SUPER: 24 GB of VRAM against 16 GB, and 960 GB/s against 736 GB/s. The measured throughput gap in our table, 40 against 58 images per minute, reflects the CUDA-first software stack that most diffusion tools assume. AMD requires the ROCm stack or Vulkan paths, which work but add configuration. Buyers who mainly generate images and enjoy tweaking software get fair value from AMD; buyers who want every ComfyUI extension to install cleanly should stay on NVIDIA.

Which 16 GB GPU gives the most diffusion speed?

The GeForce RTX 5080 is the fastest 16 GB card for diffusion in our database. TechPowerUp measured 65 SDXL Turbo images per minute from its 960 GB/s of GDDR7, ahead of the RTX 4080 SUPER's 58.

The alternative view favors the used RTX 3090: for buyers who can find one, its 24 GB of VRAM costs less than a new 5080 while trading away speed, at 45 images per minute per Puget Systems. That is the recurring trade in image generation hardware — bandwidth buys images per minute, capacity buys workflow size, and mid-range buyers choose which matters more.

What about the RTX 4070 Super and RTX 4070 Ti Super?

Both cards remain common in stores and sit between the budget and high-end tiers in NVIDIA's lineup. They are workable diffusion cards, but our benchmark database holds no measured generation figures for them, so we do not rank them numerically.

What we can say from the surrounding tiers: the RTX 4070 Ti Super carries the same 16 GB memory class as the cards above and slots below them on price, while the RTX 4070 Super targets the 12 GB class alongside cards like the RTX 3060 that long anchored budget builds. If a discounted listing beats our ranked picks' street prices, the VRAM class table above tells you which tier of workflow it supports.

What are the pros and cons of the top picks?

Each recommendation gives something up. This is the summary buyers most often need.

• RTX 5090: 120 img/min and 32 GB of VRAM; $1,999 MSRP and 575 W of heat to manage.
• RTX 4090 (used): 80 img/min with 24 GB; previous-generation card, no warranty when bought used.
• RTX 5080: fastest 16 GB card at 65 img/min; 16 GB ceiling for the largest checkpoints.
• RTX 4060 Ti 16GB: cheapest new 16 GB card at $499; 28 img/min is the slowest new-card figure we track.
• Used RTX 3090: 24 GB of capacity at used-market pricing; 45 img/min and a 350 W draw from 2020 silicon.

Notice that no pick is strong on every axis: capacity, throughput, price, and power draw trade off in every row.

Who should NOT buy a high-end diffusion GPU?

Buyers generating a handful of images per week should not buy any card in the top half of the table. The RTX 4060 Ti 16GB at $499 already produces 28 images per minute, which outpaces casual usage by a wide margin.

Buyers who need large batches only occasionally should compare against cloud rental too: our cloud pricing database lists single RTX 4090 instances at $0.34 per hour on RunPod, enough for burst generation without owning the hardware. High-end diffusion GPUs pay off when generation runs daily or latency between iterations matters to paid work.

How did we rank these GPUs?

We ranked the cards on measured SDXL Turbo throughput in ComfyUI from our benchmark database, recorded in June 2025 from Tom's Hardware, TechPowerUp, and Puget Systems, with each figure labeled by source and estimate status. Specification values come from manufacturer datasheets in our GPU database.

We report launch MSRPs only, never street prices, because listings move constantly. Rankings weigh generation throughput first, then VRAM capacity, then power draw and price. Cards without measured database entries, such as the RTX 4070 Super tier, are discussed qualitatively rather than ranked. Affiliate relationships do not influence rankings.

Frequently Asked Questions

What GPU do I need for SDXL?

A 16 GB card covers SDXL workflows with batching headroom, and even 12 GB cards run SDXL-class models per our Arc B580 estimate of 18 images per minute. The 24 GB tier matters mainly for larger checkpoints and heavy batch queues.

Is the RTX 3090 still good for image generation?

Yes. Puget Systems measured 45 SDXL Turbo images per minute, and its 24 GB of VRAM handles the same checkpoint classes as the RTX 4090. The trade-offs are speed and the risks of used hardware.

How much faster is the RTX 5090 for image generation?

According to Tom's Hardware, the RTX 5090 generates 120 SDXL Turbo images per minute against the RTX 4090's 80 and the RTX 4060 Ti 16GB's 28. The advantage comes from 1,792 GB/s of GDDR7 bandwidth plus 21,760 CUDA cores.

Can AMD GPUs run Stable Diffusion well?

The RX 7900 XTX reaches 40 images per minute on ROCm per Tom's Hardware, with 24 GB of VRAM at a $999 MSRP. It is viable, but the CUDA-first tooling around ComfyUI makes NVIDIA the lower-friction choice.

Which GPU under $1,000 is best for diffusion?

The RTX 5080 at its $999 MSRP, with 65 images per minute per TechPowerUp and 16 GB of GDDR7. A used RTX 3090 is the capacity-first alternative at the same budget.

Sources

Specifications are manufacturer datasheet values from our GPU database; benchmark figures are from the named third-party sources below.

• Tom's Hardware GPU Benchmarks 2025 — https://www.tomshardware.com/pc-components/gpus
• TechPowerUp GPU Reviews — https://www.techpowerup.com/reviews/
• Puget Systems Hardware Testing — https://www.pugetsystems.com/labs/
• StorageReview GPU Reviews (UL Procyon AI Image Generation) — https://www.storagereview.com/review/nvidia-geforce-rtx-5080-review-the-sweet-spot-for-ai-workloads
• NVIDIA Official Specifications — https://www.nvidia.com/en-us/data-center/

Related reading: our Flux.1 GPU guide, the local LLM GPU guide, and the best GPU under $500 roundup.

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