Quick Start

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

Recommended Configurations

Choose a configuration based on your use case:

🔬 Development & Testing

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

🚀 Production Training

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

💡 Research & Fine-tuning

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

Deploy in 3 Steps

Deploy a GPU instance in seconds

1. Select GPU

Go to app.spheron.ai → Deploy

Choose from recommended configurations or browse catalog:

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

2. Configure

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

3. Launch

Click Deploy Instance → Wait 30s → Copy SSH command

ssh root@your-instance-ip

Verify & Test

Check GPU

nvidia-smi  # Should show 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

# Quick install for 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
Clone repositories
Setup monitoring

See Startup Script Examples for templates.

Managing Costs

Terminate instance when done:

Go to instance dashboard → Click Terminate
Stops all charges immediately
All data permanently deleted

Tip: Use Reserved GPUs for long-term work to save 30-50% on costs.

What's Next?

Deploy AI Models

Deploy LLMs - Run Qwen, Chandra OCR, and more
AI Nodes - Gensyn, Pluralis, Inference networks

Advanced Setup

Jupyter Notebook - Browser-based development
VS Code Remote - Remote development environment
Startup Scripts - Automate configuration

Platform Features

API Reference - Automate deployments with API
Reserved GPUs - Save 30-50% with reservations
Security Guide - Best practices

Troubleshooting

SSH connection issues:

Verify correct SSH key uploaded: Check User Settings
Try explicit key: ssh -i ~/.ssh/id_ed25519 user@ip
See SSH Guide for detailed help

GPU not showing:

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

Deployment failed:

Check account balance has sufficient credits
Try different region (some may be at capacity)
Contact support via Discord

Need Help?

Community: Discord - Fast help from community and team
Docs: All Guides - Complete documentation
Support: General Info - Official channels