Quick Start

Fast GPU deployment for users with an account already configured. Deploy in under 3 minutes.

Recommended configurations

Choose a configuration based on your use case:

Development and testing

GPU: RTX 4090 (24 GB VRAM) Cost: ~$0.52/hour Best for: Prototyping, small models, testing

Production training

GPU: H100 SXM5 (80 GB VRAM) Cost: Variable (check dashboard) Best for: Large language models, production training

Research and fine-tuning

GPU: A100 (40 GB/80 GB VRAM) Cost: Variable (check dashboard) Best for: Model fine-tuning, research workloads

No GPU needed

Compute: CPU Node (from 4 vCPU) Cost: From $0.09/hour Best for: Data prep, build steps, schedulers, API workers

Deploy in 3 steps

Deploy a GPU instance in seconds

1. Select GPU

Go to app.spheron.ai and click Deploy GPUs in the sidebar.

Choose from recommended configurations or browse the catalog:

RTX 4090 for development and testing
A100 for production training
H100 for large-scale LLM work

Filters persist in the URL, so a refresh keeps your view and the link opens the same catalog for a teammate.

For a workload that never touches CUDA, click Deploy CPU instead. That page skips the catalog entirely and goes straight to configuration.

2. Configure

Region: Closest to your location
OS: Ubuntu 22.04 LTS
SSH Key: Select from your uploaded keys
Review pricing in the order summary

3. Launch

Click Deploy Instance and wait about 30 seconds. Copy the SSH command from the instance details panel in the dashboard. The username and port vary by provider.

# Spheron AI provider - username is ubuntu
ssh ubuntu@<your-instance-ip>

Verify and test

Check GPU

nvidia-smi  # Shows GPU model, memory, driver

Quick tests

# Test CUDA
nvcc --version
 
# Test PyTorch (if installed)
python3 -c "import torch; print(torch.cuda.is_available())"
 
# Monitor GPU
nvidia-smi -l 1

Install ML stack

# Install common libraries
pip install torch torchvision transformers accelerate bitsandbytes
 
# Or use conda
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Advanced options

Startup scripts

Automate setup with cloud-init scripts. Add during deployment to:

Install dependencies on first boot
Configure environment variables
Clone repositories
Set up monitoring

See Startup Script examples for templates.

Managing costs

Stop the instance when you will return to it:

Go to the instance dashboard and click Stop
The GPU stops billing; the disk and its contents are preserved
Start it again on the same disk and the same public IP
Available on Spheron AI and Spheron ES

Terminate the instance when you are done for good:

Go to the instance dashboard and click Terminate
Stops all charges immediately
All data is permanently deleted

Troubleshooting

SSH connection issues:

Verify the correct SSH key is uploaded: check User Settings
Try with an explicit key path: ssh -i ~/.ssh/id_ed25519 <user>@<ip>
See SSH Guide for detailed help

GPU not showing:

Wait 30 seconds after deployment (drivers may still be loading)
Run nvidia-smi to verify
Reboot if needed: sudo reboot

Deployment failed:

Check that account balance has sufficient credits
Try a different region (some may be at capacity)
Contact support via the chat widget on app.spheron.ai

What's next

Deploy AI models

Deploy LLMs: Run Qwen, Chandra OCR, and more
AI Nodes: Gonka AI, Pluralis

Advanced setup

Jupyter Notebook: Browser-based development
VS Code Remote: Remote development environment
Startup Scripts: Automate configuration
Templates & Images: Copy-ready startup scripts for common stacks

Platform features

CPU Nodes: CPU-only instances from $0.09/hr
Instance Lifecycle: Stop, start, and restart an instance
API Reference: Automate deployments with the API
Reserved GPUs: Save 30-50% with reservations
Security guide: Best practices