Launch H200, H100, A100, L40S, or RTX 4090 instances on-demand. Pre-configured templates (PyTorch, vLLM, Stable Diffusion, ComfyUI) boot instantly. SSH + Jupyter + Web terminal. Pay per second of actual GPU time. Receipt-signed session start + stop.
Reserved (longer-term) and reverse-auction (cheapest) modes also available. Below shows on-demand rates.
| GPU | VRAM | vCPU | RAM | Storage | On-demand |
|---|---|---|---|---|---|
| H200 | 141 GB | 32 | 256 GB | 500 GB NVMe | $24.00/hr |
| H100 | 80 GB | 26 | 200 GB | 400 GB NVMe | $22.00/hr |
| A100 | 80 GB | 22 | 156 GB | 300 GB NVMe | $14.00/hr |
| L40S | 48 GB | 16 | 96 GB | 200 GB NVMe | $5.40/hr |
| RTX 5090 | 32 GB | 12 | 64 GB | 200 GB NVMe | $2.40/hr |
| RTX 4090 | 24 GB | 10 | 48 GB | 150 GB NVMe | $1.20/hr |
Reverse-auction mode is ~12.75% cheaper on average. See ROI Calculator.
CUDA 12.4, common HF libraries, Jupyter. Most-used template.
OpenAI-compatible inference server. Mount your weights, get an endpoint.
ComfyUI + AUTOMATIC1111. Drop in checkpoints + LoRAs. Web UI ready.
Blender, FFmpeg, RunwayML-compatible. Headless render farm config.
Transformers + Datasets + Accelerate. Trainer setup pre-wired.
Ubuntu 22.04 + CUDA + nothing else. Build your own from a clean slate.
Run a DCS Agent Studio composition on dedicated GPU. Auto-receipt signing.
Bring your own Docker image. Pull from public registry or DCS-hosted private.
Pre-warmed template images. Most launches complete in <30 seconds.
Stop saves money instantly. No "rounded up to the hour" gotchas.
Storage survives stop/start. Don't lose your weights when you cycle the GPU.
Three ways in. SSH key uploaded at launch. Jupyter token in dashboard.
Start, stop, snapshot, destroy — all signed. Audit-grade billing trail.
US-East, US-West, EU-W, Asia-South. Per-region pricing identical; latency-based routing.
Per-second billing. Pre-configured templates. Audit-grade receipts.