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
A dead-simple UI for training Flux LoRAs, built on the Kohya scripts. Flux Gym is the simplest UI for training Flux LoRAs, built on the Kohya scripts with sensible defaults and low-VRAM modes. Upload images, write captions, pick a VRAM profile, press train. 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: Flux Gym 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: the 20 GB profile trains a Flux dev LoRA in about an hour. | Deploy |
| Better | RTX 5090 | 32 GB | $0.318 | $0.159 | 32 GB for faster runs and higher resolutions. | Deploy |
| Best | RTX PRO 6000 WS | 96 GB | $1.097 | $0.548 | 96 GB for full-precision Flux with big batches. | 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 flux-gym \
--disk 100 --volume models:/workspace/models
✓ instance i-7a41c0e2 running (27.9s)
# Flux Gym · RTX 4090 · $0.262/hr · per second
# https://i-7a41c0e2.powergpu.io:8888 (TLS)
$ powergpu stop i-7a41c0e2 # billing ends this second
| Image | powergpu/fluxgym |
|---|---|
| CUDA | inherits host driver (any 12.x / 13.x) |
| Access | also builds for ARM hosts · 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 $0.131/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.
Flux Gym's 12 GB and 16 GB profiles use quantized bases and gradient checkpointing. They work on a 16 GB card at reduced speed; 24 GB is the comfortable minimum.
Top up in crypto, benchmark us against your current provider. Per-second billing, fixed prices ≥ 30% below market — cancel by just stopping the instance.