Eight gigabytes does not sound like much of a gap until a video model needs exactly those eight to load without being cut down. The RTX 5090 with 32GB against the RTX 4090 with 24GB is, for diffusion work, mostly a story about what those extra gigabytes and the faster memory let you do. The 5090 loads full-quality video models the 4090 cannot fit unquantised, while the 4090 stays a strong, sensible card for image work and lighter video.

Quick Answer

The RTX 5090's 32GB and much higher memory bandwidth let it run full-precision FP16 Flux and large video models like Wan 2.2 14B without quantising or offloading, which the 4090's 24GB cannot do unaided. The 4090 remains capable for 720p Wan, tiled 4K image work and FP8 Flux. If you generate video locally, the extra 8GB is the deciding factor; for pure image work, the 4090 still holds up.

What The Extra 8GB Unlocks

VRAM is a hard wall in diffusion: a model either fits or it does not. The 5090's 32GB is a third more than the 4090's 24GB, and that headroom is exactly what large video pipelines need. The 5090 can hold FP16 Flux and a 14B video model without dropping to lower precision or spilling into system RAM, so it runs them at full quality and keeps everything resident at once.

The 4090, by contrast, must lean on memory-efficient attention to keep FP16 Flux inside 24GB and may run out of room on the heaviest video models without quantising them first. It still works, it just works with more compromises on the largest jobs.

Bandwidth, Not Just Capacity

Capacity is only half of it. The 5090's memory bandwidth is dramatically higher than the 4090's, which speeds up the constant shuffling of data during generation, especially on large models and video sequences where memory traffic is heavy. The practical result is faster batch turnaround on big jobs, on top of simply being able to fit them. If your bottleneck is how long a queue of renders takes rather than cost per image, that combination of more VRAM and more bandwidth is what shortens the wait.

Where The 4090 Still Makes Sense

The 4090 is far from obsolete for AI art. For image generation, including tiled 4K work, FP8 Flux, and SDXL, it is quick and capable, and for 720p video with models that fit its 24GB it handles the load well. It is the right card for someone focused on images and lighter video who does not need to run the very largest models at full precision. The decision comes down to your workload, not bragging rights.

If your work is video and large-model diffusion, the RTX 5090 graphics cards at Evetech are the match, and complete builds tuned for this sit on the AI PCs range.

Frequently Asked Questions

Is the RTX 5090 worth it over the 4090 for diffusion?

If you run large video models or want FP16 Flux and a 14B video model loaded at full quality without quantising, yes, the 32GB and higher bandwidth justify it. For mainly image work, the 4090's 24GB still does the job and the upgrade is harder to justify.

Can the RTX 4090 run FP16 Flux?

It can, but tightly: it needs memory-efficient attention to stay inside 24GB and has little spare room. The 5090 runs FP16 Flux comfortably with headroom to spare, so the 4090 is workable here while the 5090 is relaxed.

Does the 4090 handle AI video at all?

Yes, for 720p Wan and other video models that fit within 24GB, often after quantisation for the larger ones. The 5090's extra VRAM matters most when you want longer sequences, higher resolution, or the biggest models at full precision.

What does the extra memory bandwidth actually do?

It speeds the heavy data movement during generation, so big jobs and video sequences process faster on the 5090 beyond just fitting in memory. If your concern is total render time on large queues, that bandwidth shortens it noticeably.

Deciding between 24GB and 32GB for your diffusion workflow? Compare current cards on the Evetech GPU best sellers and pick the VRAM that matches whether you generate images or full-quality local video.