Running in ~30 s
Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this.
Products
Developers & company
GPUs · on-demand per hour
Template · Video generation · CUDA 12.9
Run Wan and other video-generation models on modest VRAM, with a simple UI. Wan2GP ("Wan for the GPU poor") runs Wan 2.x, Hunyuan and other text- and image-to-video models with aggressive offloading and quantization, so 720p clips are possible on 16–24 GB cards. A simple web UI hides the memory tricks. From $0.131/hr on an interruptible RTX 4090.
Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this.
You pay the GPU price only: Wan2GP on a RTX 4090 is $0.262/hr on-demand, $0.131/hr interruptible, billed per second.
Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable.
Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — Jupyter and SSH behind your own credentials.
Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks.
| Tier | GPU | VRAM | On-demand | Interruptible | Why this card | Action |
|---|---|---|---|---|---|---|
| Good | RTX 4090 | 24 GB | $0.262 | $0.131 | 24 GB: Wan 2.1 14B at 480p–720p with offloading. | Deploy |
| Better | RTX 5090 | 32 GB | $0.318 | $0.159 | 32 GB GDDR7 — noticeably faster clips and fewer offload stalls. | Deploy |
| Best | H100 PCIE | 80 GB | $2.147 | $1.073 | 80 GB for full-precision video models without offloading. | Deploy |
Need more VRAM? The full catalogue lists all 76 models with live availability; the VRAM guide sizes models to cards.
Pick the template in the console deploy bar, or script it:
$ powergpu launch --gpu rtx-4090 --template wan2gp \
--disk 100 --volume models:/workspace/models
✓ instance i-7a41c0e2 running (27.9s)
# Wan2GP · RTX 4090 · $0.262/hr · per second
# https://i-7a41c0e2.powergpu.io:8888 (TLS)
$ powergpu stop i-7a41c0e2 # billing ends this second
| Image | powergpu/wan2gp |
|---|---|
| CUDA | CUDA 12.9 |
| Access | SSH shell · JupyterLab on a mapped port |
| Category | Video generation |
| Storage | Instance NVMe disk (sized at deploy) + optional network volumes |
| Billing | GPU price only, per second — no template fee, no setup fee |
Environment variables, ports and custom images are covered in the template docs.
Only the GPU price — the template is free. From $0.131/hr on an interruptible RTX 4090, $0.262/hr on-demand on a RTX 4090. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month.
About 30 seconds from the deploy click: the image is pre-cached on hosts, ports are mapped and TLS is terminated for you. Restarting a stopped instance is faster, and your disk is exactly as you left it.
Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy.
Roughly 8–15 minutes at 720p on an RTX 4090 with offloading, 4–8 minutes on an 80 GB H100 without it. Batch overnight on interruptible pricing to halve the cost per clip.
Top up in crypto, benchmark us against your current provider. Per-second billing, fixed prices ≥ 30% below market — cancel by just stopping the instance.