Alibaba dropped Wan 2.2 14B under Apache 2.0 this May, and ComfyUI integration landed within days. The early numbers are interesting — Wan 2.2 is lighter than HunyuanVideo at comparable resolutions, which finally puts open-source video generation in reach of 16GB cards (with caveats). The Apache 2.0 license also means commercial use is fine, so this isn’t just a tech demo. It’s the model people are actually shipping social content with right now.
Quick answer: The RTX 4090 24GB is the best local pick for Wan 2.2 14B at FP16, while the RTX 4070 Ti Super 16GB handles int8/Q4 quants comfortably. If you want headroom for 5-second 720p clips at full precision without offload tricks, go RTX 5090.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB GDDR6X clears Wan 2.2 14B at FP16 for short clips and stays comfortable with int8 for longer sequences. The sweet spot price-per-performance pick of 2026.
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Who this is for
You want to run Wan 2.2 locally — image-to-video, text-to-video, or short loops for social content — and you’re tired of cloud queues and per-minute billing. This guide assumes you have ComfyUI installed (or are willing to), and want hard VRAM numbers instead of marketing-speak. If you’re brand new to AI video, start with our best GPU for AI video primer first, then come back here once you’ve narrowed it to Wan.
VRAM requirements for Wan 2.2
Wan 2.2 ships in two flavors right now: the headline 14B model and a leaner 5B variant. The 5B is dramatically more forgiving on VRAM, but quality drops are noticeable — texture stability and prompt adherence both take a hit.
| Variant + Quant | Minimum VRAM | Comfortable VRAM | Resolution Tested |
|---|---|---|---|
| Wan 2.2 14B FP16 | 24GB | 28-32GB | 720p, 5-sec |
| Wan 2.2 14B int8 | 16GB | 20GB | 720p, 5-sec |
| Wan 2.2 14B Q4 | 12GB | 16GB | 480p, 3-sec |
| Wan 2.2 5B FP16 | 12GB | 16GB | 720p, 5-sec |
| Wan 2.2 5B int8 | 8GB | 12GB | 480p, 5-sec |
In our experience running both, the 14B at int8 on a 16GB card is the practical floor for results you’d actually post. Q4 on 12GB works, but artifacts creep in fast on motion-heavy scenes — faces deform mid-motion, text in backgrounds smears, and complex camera movements fall apart. A second factor most VRAM tables ignore: context length. Wan 2.2 scales VRAM roughly linearly with clip duration. A 5-second clip is the tested baseline; pushing to 8-10 seconds adds 30-50% more VRAM at the same resolution.
Generation times per GPU (Wan 2.2 14B)
Numbers below are community-aggregated estimates from ComfyUI Wan 2.2 workflows in early May 2026. Treat them as ballparks — sampler choice, step count, and motion complexity move these meaningfully.
| GPU | VRAM | 5-sec 480p (int8) | 5-sec 720p (int8) | 5-sec 720p (FP16) |
|---|---|---|---|---|
| RTX 5090 | 32GB | ~2 min | ~5 min | ~7 min |
| RTX 4090 | 24GB | ~3 min | ~8 min | ~12 min (tight) |
| RTX 3090 | 24GB | ~5 min | ~13 min | ~19 min |
| RTX 5080 | 16GB | ~4 min | ~10 min | offload only |
| RTX 5070 Ti | 16GB | ~5 min | ~12 min | offload only |
| RTX 4070 Ti Super | 16GB | ~6 min | ~14 min | offload only |
| RTX 4060 Ti 16GB | 16GB | ~10 min | ~24 min | not recommended |
| RTX 3060 12GB | 12GB | ~14 min (Q4) | not recommended | no |
The 4060 Ti 16GB row is the most surprising — it technically fits Wan 2.2 14B int8, but the 128-bit memory bus chokes throughput. Bandwidth matters more than raw VRAM once you clear the minimum.
Best GPU picks for Wan 2.2
RTX 5090 — fastest local option
32GB of GDDR7 and ~1.8TB/s of memory bandwidth is overkill for Wan 2.2 5B and exactly right for 14B at FP16. If you’re doing batch generation or planning to train LoRAs on Wan checkpoints, the headroom matters. At ~$2,000 it’s not cheap, but it’s the only consumer card that runs the 14B comfortably at FP16 without quantization compromises. We recommend against this card for anyone generating fewer than 20 clips a week — the marginal speed gain over a 4090 isn’t worth $400 unless you’re rendering daily.
Check RTX 5090 on Amazon→Buy on Shopee SG→RTX 4090 — best value for serious local use
24GB hits the Wan 2.2 14B FP16 minimum and breezes through int8. Generation times are roughly 1.7x slower than the 5090, which is the right trade for a $2,700 price gap. This is our default recommendation for anyone generating video weekly. We also flag it across our ComfyUI buying guide for the same reasons — it’s the card that ages best across video, image, and LoRA training.
RTX 4070 Ti Super — the 16GB sweet spot
At ~$800, this is the cheapest card we’d buy specifically for Wan 2.2. 16GB handles 14B int8 cleanly at 720p, and the memory bandwidth (672 GB/s) keeps generation times sane. The RTX 5070 Ti performs about the same but now costs ~$1,050, so it is only worth it if the 4070 Ti Super is out of stock.
RTX 3090 — used market value play
If you can find one at ~$700 used and trust the seller, 24GB GDDR6X runs Wan 2.2 14B at FP16. Generation is meaningfully slower than the 4090 (memory bandwidth gap is real — 936 GB/s vs 1008 GB/s plus the architectural gap), but the dollar-per-VRAM math is hard to beat for hobby use. Watch out for ex-mining cards: high hours don’t kill 3090s outright, but thermal pads on the backside VRAM degrade and need replacing. Budget another $30 and an afternoon for that if you go used.
Don’t bother: RTX 4060 Ti 16GB and below
This is the contrarian take. On paper, the 4060 Ti 16GB looks like a Wan 2.2 bargain. In practice, the 128-bit bus turns generation into a slideshow — a 720p int8 clip that takes 8 minutes on a 4090 takes 24 minutes here. Spend the extra $375 on a 4070 Ti Super and stop suffering. The RTX 3060 12GB is fine for Wan 2.2 5B at low resolution, but the 14B model isn’t really its job.
Which GPU should YOU buy?
- Generating Wan 2.2 daily, want max headroom? RTX 5090 32GB — FP16, no quantization, room for batches.
- Weekly Wan 2.2 user, want best dollar-per-frame? RTX 4090 24GB — runs everything Wan ships at FP16 or int8.
- First serious AI video build, $800 budget? RTX 4070 Ti Super 16GB — int8 14B is your sweet spot.
- Already have a 3090? You’re fine. Don’t upgrade unless you’re doing this professionally.
- Stuck with 12GB? Use Wan 2.2 5B or Q4 14B at 480p — and consider cloud for anything you’d publish.
- Training Wan LoRAs or fine-tuning? Skip consumer cards. See our AI research GPU guide — for research workloads on diffusion video models you want 48GB+ (RTX 6000 Ada or rented H100s), full stop.
Cloud is still reasonable for Wan 2.2
Wan 2.2 14B at FP16 on an A100 or H100 takes 3-5 minutes per 5-second 720p clip. At RunPod’s spot pricing, that’s roughly $0.20-0.40 per finished clip. If you generate fewer than ~50 clips a month, you’ll never break even on a 4090 purchase. Power draw also matters — a 4090 pulls 350-450W during video generation, which adds up on a metered electric bill. Cloud GPUs externalize that cost. The other underrated cloud benefit: H100s with NVLink let you run Wan 2.2 14B at FP16 with 10-second clips, which no consumer card can match yet.
Run Wan 2.2 on RunPod→ Try Vast.ai for AI video→For high-volume daily use, local pays back inside 6 months. For experimentation, rent.
Common mistakes to avoid
- Treating Wan 2.2 like HunyuanVideo. Wan is lighter — you don’t always need 24GB. See our HunyuanVideo GPU breakdown for the contrast. Picking your GPU by the wrong model’s requirements costs you ~$400-600.
- Skipping int8 quantization on a 16GB card. ComfyUI’s GGUF nodes for Wan 2.2 are stable as of May 2026. Use them. FP16 on 16GB will OOM on anything past 3 seconds.
- Buying a 4060 Ti 16GB for the VRAM number. Memory bandwidth is the real bottleneck for video diffusion. The 4070 Ti Super is night-and-day faster despite the same VRAM.
- Ignoring storage. Wan 2.2 14B FP16 weights are ~28GB on disk. Add ComfyUI, LoRAs, and output clips and you’ll fill a 1TB SSD inside a month of active use.
Final verdict
| Use case | Recommendation | Why |
|---|---|---|
| Maximum local performance | RTX 5090 32GB | FP16 14B with batch headroom |
| Best value for serious users | RTX 4090 24GB | FP16 14B at ~$2,700 less than 5090 |
| Budget local entry | RTX 4070 Ti Super 16GB | int8 14B sweet spot at ~$800 |
| Used market play | RTX 3090 24GB | FP16 14B if priced right |
| Hobby / 5B only | RTX 3060 12GB | Wan 2.2 5B at 480p, that’s the ceiling |
| Occasional use | Cloud (RunPod / Vast.ai) | Cheaper than hardware under ~50 clips/month |
NVIDIA GeForce RTX 4090
24GB GDDR6XThe RTX 4090's 24GB GDDR6X is the practical sweet spot for Wan 2.2 14B in 2026 — FP16 for short clips, int8 for everything else, and headroom for ComfyUI workflows you haven't installed yet.
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For Wan 2.2 specifically, the RTX 4090 at 24GB is the GPU we’d buy with our own money today — fast enough at FP16, comfortable at int8, and priced where the math still works for serious local users without forcing a $4,900 commitment to the 5090 tier.
Frequently asked questions
How much VRAM does Wan 2.2 14B need?
Roughly 14-20GB at int8 or Q4 quantization for 720p 5-second clips, and around 28GB at FP16. A 16GB GPU can handle int8 comfortably; FP16 wants 24GB or more.
Can I run Wan 2.2 on an RTX 3060 12GB?
Yes for the Wan 2.2 5B variant at 480p, and Q4-quantized 14B with patience. Don’t expect FP16 14B performance — that’s a 24GB-class job.
Is the RTX 4060 Ti 16GB good for Wan 2.2?
Not really. The 16GB fits, but the 128-bit memory bus makes generation roughly 3x slower than an RTX 4070 Ti Super at the same VRAM tier. Spend the extra for the wider bus.
Should I buy a GPU or rent cloud for Wan 2.2?
If you generate under ~50 short clips per month, RunPod or Vast.ai on an A100 or H100 is cheaper than buying a 4090. Above that threshold, local hardware pays back in about 6 months.