Running in ~30 s
Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this.
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GPUs · on-demand per hour
Template · Training & fine-tuning · CUDA 12.6
Train SDXL and Flux image LoRAs in a browser GUI — dataset in, .safetensors out. Kohya's GUI is the standard trainer for Stable Diffusion, SDXL and Flux LoRAs: dataset prep, captioning helpers, bucketed resolutions and a browser UI over the sd-scripts. Twenty to forty images in, a .safetensors LoRA out, usually within an hour on a 4090. From $0.052/hr on an interruptible RTX 3090.
Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this.
You pay the GPU price only: Kohya's GUI on a RTX 3090 is $0.104/hr on-demand, $0.052/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 3090 | 24 GB | $0.104 | $0.052 | 24 GB at the lowest rate for SDXL LoRAs. | Deploy |
| Better | RTX 4090 | 24 GB | $0.262 | $0.131 | The community default: SDXL and Flux LoRAs in 30–60 minutes. | Deploy |
| Best | RTX 5090 | 32 GB | $0.318 | $0.159 | 32 GB for Flux full-precision training and larger batch sizes. | 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-3090 --template kohya-s-gui \
--disk 100 --volume models:/workspace/models
✓ instance i-7a41c0e2 running (27.9s)
# Kohya's GUI · RTX 3090 · $0.104/hr · per second
# https://i-7a41c0e2.powergpu.io:8888 (TLS)
$ powergpu stop i-7a41c0e2 # billing ends this second
| Image | powergpu/kohyas-gui |
|---|---|
| CUDA | CUDA 12.6 |
| Access | SSH shell · JupyterLab on a mapped port |
| Category | Training & fine-tuning |
| 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 $0.052/hr on an interruptible RTX 3090, $0.104/hr on-demand on a RTX 3090. 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.
A typical 30-image SDXL LoRA at 1,500 steps takes 30–45 minutes on an RTX 4090 — well under a dollar on interruptible pricing.
Top up in crypto, benchmark us against your current provider. Per-second billing, fixed prices ≥ 30% below market — cancel by just stopping the instance.