Can RTX 5090 Run Your Own AI Video Generation Studio?

Yes, for one person making short clips, with some compromises. A GeForce RTX 5090 with 32 GB of VRAM can run today's leading open video models locally, including Wan 2.2, HunyuanVideo 1.5 and LTX-2, at 480p to 720p in ComfyUI. The catch is that the biggest models were built for 80 GB data-centre GPUs. On a 5090 they run as fp8 or GGUF quantised versions, with parts offloaded to system RAM and fewer sampling steps. Published 5090 timings are thin: one comparison puts a Wan 2.1 image-to-video job at 419 seconds on a 5090 against 763 seconds on an RTX 4090. Most other figures are community reports, and we label them as such below.

What do the main open video models need?

Source: each model's official GitHub README and the ComfyUI Wan 2.2 docs (fetched 6 Nov 2026); NVIDIA LTX-2 ComfyUI guide (17 Mar 2026). "With offload" means part of the model sits in system RAM, which saves VRAM but costs speed.

Two points stand out. First, 32 GB is not enough to run Wan 2.2's 14B models the way their developers do, so 5090 owners rely on the community's quantised versions. Second, the newest closed models are not an option at all. Alibaba's Wan 3.0 (August 2026) has no downloadable weights, so it runs only through the cloud, whatever GPU you own.

How fast is an RTX 5090 at video generation?

There is no like-for-like lab table comparing the 5090, 5080, 4090 and RTX PRO 6000 on Wan 2.2 at 720p. These are the figures that exist:

• Wan 2.1 image-to-video, ComfyUI (VALDI, 2026): 419 seconds on a 5090 vs 763 seconds on a 4090 over 100+ runs per card. That's about 45% less time, or about 8.6 clips an hour against 4.7 (our arithmetic). Peak VRAM was 24.2 GB vs 15.6 GB, and average power about 520 W vs 235 W. VALDI didn't state resolution, frame count or steps.
• Wan 2.2 I2V-A14B, 4-step quantised, 720p (TurboDiffusion): the project claims 38 seconds on a 5090. A user measured about 78–83 seconds once text encoding and VAE decoding were included (GitHub issue, community report).
• Wan 2.2 14B at 1280×720×81 frames on an RTX 5080: about 28 seconds per iteration with torch.compile and SageAttention (Reddit, community report). The poster's guess of about 15 seconds for a 5090 was not measured.
• For context, on a 24 GB RTX 4090 (computingforgeeks, 23 Sep 2026): Wan 2.2 TI2V-5B at 1280×704, 121 frames and 50 steps took 667 seconds. Wan 2.2 14B at Q4_K_M, 832×480, 81 frames and 20 steps took 516 seconds at a 14.9 GB peak.

The pattern is clear even if the numbers are rough. A five-second 720p clip takes minutes, not seconds, unless you use a step-distilled model. The 5090 roughly halves the wait compared with a 4090 in the one cleaner comparison available.

A 5090 turns local video generation from an overnight experiment into something you can iterate on during the day, but only with quantised models and short clips.

How do 32 GB users fit 14B video models?

• fp8 weights: ComfyUI publishes fp8_scaled versions of the Wan 2.2 14B models, roughly halving their size compared with 16-bit.
• GGUF quantisation: a Q4_K_M Wan 2.2 14B used 14.9 GB on a 4090 in the test above, leaving room for longer clips on a 32 GB card.
• Offloading: Wan's --offload_model and --t5_cpu options, HunyuanVideo's CPU offload and ComfyUI's weight streaming move parts of the model to system RAM. NVIDIA says weight streaming carries a performance cost. Plan on 64 GB of system RAM, as NVIDIA's LTX-2 guide does.
• Step distillation: Lightning or Turbo LoRAs cut Wan's sampling to about four steps. HunyuanVideo 1.5 claims "up to 6x" from its distilled version.
• NVFP4 and NVFP8 checkpoints: NVIDIA says NVFP4 in ComfyUI on RTX 50 cards is "up to" 3x faster with 60% less VRAM, naming LTX-2 among others but not Wan 2.2. That is a vendor claim without published test conditions.

What does a practical local workflow look like?

Most 5090 owners work in ComfyUI. A typical image-to-video loop looks like this:

1. Generate or choose a keyframe image.
2. Draft the motion at 480p with a distilled 4–8 step model to check the idea quickly.
3. Re-run the keepers at 720p with more steps.
4. Upscale or interpolate at the end.
5. Queue longer or higher-resolution jobs to run overnight.

Keep the software current. Blackwell GPUs need PyTorch 2.7 or later built for CUDA 12.8. Tutorials that pin older builds, such as PyTorch 2.5 with CUDA 12.1, will not run on a 5090. Most 2025 complaints that a "5090 doesn't work" with a tool traced back to old wheels.

Power and heat are part of the workflow. VALDI measured about 520 W average on the 5090 during video jobs, so a long batch is a sustained full load, not a gaming-style burst. On VALDI's averages, each clip used about 0.06 kWh on the 5090 against 0.05 kWh on the 4090. The 5090 finished faster but drew more than twice the power (our arithmetic). As an illustration, a whole system drawing 0.7 kW for eight hours uses 5.6 kWh, about S$1.74 a day at SP Group's Q4 2026 tariff of 31.16¢/kWh including GST. Our posts on the 5090's 575 W appetite and undervolting cover how to bring that down.

When should you look beyond an RTX 5090?

• You want full-precision 14B models or longer, higher-resolution clips. Wan 2.2 A14B and the original HunyuanVideo are specified for 60–80 GB. A 96 GB RTX PRO 6000 Blackwell gets much closer to the developers' setups. It is not unlimited, though: one test ran out of memory on Wan2.1-VACE-14B in BF16 even on the PRO 6000. See who actually needs 96 GB and RTX 5090 vs RTX PRO 6000.
• Several people need to generate at once. One 5090 runs one heavy video job at a time.
• You plan to offer generation as a hosted service. NVIDIA's GeForce driver licence states the software "is not licensed for datacenter deployment". Read the licence, or speak to us about professional cards.
• The model you want is closed. Wan 3.0 and most commercial video models are cloud-only.

Memory limits on the AI side are covered in why AI developers still complain about 32 GB, and still-image work in do AI artists really need an RTX 5090?

Buying in Singapore

RTX 5090 graphics cards are not in stock at SourceIT at the time of writing; ask us about availability and a quotation. For 96 GB on one GPU, the RTX PRO 6000 Blackwell Workstation Edition is S$29,100 and the 300 W Max-Q is also S$29,100. For a compact system, the ASUS ROG NUC 16 Edition 20 is S$11,888.40. It uses the RTX 5090 Laptop GPU with 24 GB, which runs at roughly half the desktop card's speed. All prices listed 6 Nov 2026; prices change, so ask for a current quotation. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Can an RTX 5090 run Wan 2.2 14B? Yes, using ComfyUI's fp8_scaled or GGUF quantised versions, often with some offloading to system RAM. The developers' own single-GPU setup asks for at least 80 GB of VRAM, which no GeForce card has.

How long does a short AI video take on an RTX 5090? Minutes rather than seconds for a five-second 720p clip at normal step counts. One published comparison measured 419 seconds for a Wan 2.1 image-to-video job on a 5090, against 763 seconds on an RTX 4090. Step-distilled 4-step models can bring that down to around a minute or two, per community reports.

How much system RAM do I need for local video generation? NVIDIA's LTX-2 guide recommends 64 GB. Offloading parts of a model to system memory is common on 32 GB cards, so more RAM gives you more room.

Can I run Wan 3.0 locally? No. Wan 3.0 has no downloadable weights; the open-weight line ends at Wan 2.2.

Is the RTX 5090 Laptop GPU good for video generation? It works for smaller models, but it has 24 GB of VRAM and roughly half the desktop card's performance. It is a different chip from the desktop RTX 5090.