On-Demand GPU Quick Start - Hyperbolic Labs

Get your first H100 or H200 GPU instance running on Hyperbolic in just 5 minutes. This guide walks you through account setup, funding, SSH configuration, and launching your first enterprise-grade GPU cluster.

Prerequisites

Before you begin, make sure you have:

SSH Key Pair

Required for secure instance access. We’ll show you how to create one if needed.

Terminal Access

Basic command line familiarity for SSH connections and running commands.

Step 1: Create Your Account

Visit app.hyperbolic.ai and create your account using:

Email address
Google OAuth
GitHub OAuth

Verify Email

Check your inbox for a verification email and click the link to activate your account.

Step 2: Generate & Add SSH Key

SSH keys are required for instance access. Password authentication is disabled for security.

macOS/Linux
Windows

Generate a new SSH key

# Generate ED25519 key (recommended)
ssh-keygen -t ed25519 -C "[email protected]"

# Or generate RSA key (legacy)
ssh-keygen -t rsa -b 4096 -C "[email protected]"

Copy your public key

# For ED25519
cat ~/.ssh/id_ed25519.pub

# For RSA
cat ~/.ssh/id_rsa.pub

Using PowerShell

# Generate key
ssh-keygen -t ed25519 -C "[email protected]"

# Display public key
type $env:USERPROFILE\.ssh\id_ed25519.pub

Using PuTTYgen

Download PuTTYgen
Click “Generate” and move mouse randomly
Save private key
Copy public key from text area

Add SSH Key to Hyperbolic

Go to Account Settings
Navigate to “SSH Keys” section
Paste your public key
Click the “Save” button on bottom right

Step 3: Fund Your Account

Add credits to rent GPUs. New users get $10 free, but you can add more for extended usage.

Credit Card

Go to Billing
Click “Add Funds”
Enter amount of credits you want to add
Click “Pay Now”
Complete payment via Stripe

Credit card payments are processed instantly and securely via Stripe.

Step 4: Choose Your GPU

Browse available GPUs and select based on your needs:

GPU Selection Guide

Use Case	Recommended GPU	Price	Why
Model Development	H100 80GB	$1.49/hr	Industry standard for AI development
Production Training	H100 80GB	$1.49/hr	Fast training, reliable performance
Large Models (70B+)	H200 141GB	$2.49/hr	Maximum memory for huge models
Distributed Training	H100 + InfiniBand	$1.99/hr	Multi-node clusters with fast interconnect
Ultra-Large Models	H200 + InfiniBand	$2.99/hr	Next-gen performance for cutting-edge AI

Browse On-Demand GPUs

View real-time GPU availability and pricing

Step 5: Launch Your First Instance

Quick Launch (Web UI)

Select GPU

Go to Hyperbolic
Filter by GPU type, price, or region
Click on an available GPU listing

Configure Instance

Storage: Add if needed (optional)
Label: Name your instance (optional)

Launch

Review configuration and pricing
Click “Start Building”
Instance launches in less than 20 minutes
Copy the SSH connection string

Step 6: Connect to Your Instance

Once your instance is running, connect via SSH:

# Basic connection
ssh ubuntu@<instance-ip>

# With specific key file
ssh -i ~/.ssh/id_ed25519 ubuntu@<instance-ip>

First Commands to Run

# Check GPU status
nvidia-smi

# Check CUDA version
nvcc --version

# Check available disk space
df -h

# Check Python and pip
python3 --version
pip3 --version

# Monitor GPU usage in real-time
watch -n 1 nvidia-smi

Step 7: Start Building

Quick Examples

Train a Model
Run Jupyter
Fine-tune LLM

# train.py
import torch
import torch.nn as nn
from torch.utils.data import DataLoader

# Check GPU availability
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")

# Your model
model = YourModel().to(device)

# Training loop
for epoch in range(num_epochs):
    for batch in dataloader:
        inputs, labels = batch
        inputs, labels = inputs.to(device), labels.to(device)
        # Training code here

Run with:

python3 train.py

# Install Jupyter if not present
pip install jupyter

# Start Jupyter with no browser
jupyter notebook --no-browser --port=8888

# From your local machine, forward the port:
# ssh -L 8888:localhost:8888 ubuntu@<instance-ip>
# Then open: http://localhost:8888

# Clone your repo or upload data
git clone https://github.com/your-repo/llm-finetuning.git
cd llm-finetuning

# Install requirements
pip install -r requirements.txt

# Run fine-tuning
python finetune.py \
  --model_name "meta-llama/Llama-2-7b-hf" \
  --dataset "your-dataset" \
  --output_dir "./fine-tuned-model"

Managing Your Instance

Monitor Usage

Check your instance status and billing:

# On the instance - check GPU utilization
nvidia-smi

# Check running processes
ps aux | grep python

# Monitor system resources
htop

Save Your Work

Always save your work regularly to persistent storage or cloud backups!

# Save to persistent storage
cp -r /workspace/results /mnt/storage/

# Upload to cloud storage
aws s3 cp model.pth s3://your-bucket/models/

# Push to git
git add .
git commit -m "Save checkpoint"
git push

Stop or Terminate

Via Web UI

Go to My Instances
Find your running instance
Click “Terminate”

Common Issues & Solutions

If you’re still experiencing issues after trying these solutions, please contact us at [email protected] or use the Intercom chat widget in the bottom-right corner for immediate assistance.

Connection Issues

Permission denied (publickey)

Problem: SSH key not recognizedSolution:

# Check you're using the right key
ssh -i ~/.ssh/correct_key ubuntu@<ip>

# Verify key is added to account
# Go to app.hyperbolic.ai/settings

Connection timeout

Problem: Cannot reach instanceSolution:

Check instance is running in dashboard
Verify IP address is correct
Check firewall/security group settings
Try different region if persistent

Host key verification failed

Problem: SSH host key changedSolution:

# Remove old host key
ssh-keygen -R <instance-ip>

# Then reconnect
ssh ubuntu@<instance-ip>

GPU Issues

CUDA out of memory

Problem: GPU memory exhaustedSolution:

# Reduce batch size
batch_size = 16  # Lower this

# Use gradient accumulation
accumulation_steps = 4

# Clear cache
torch.cuda.empty_cache()

# Use mixed precision
from torch.cuda.amp import autocast
with autocast():
    output = model(input)

GPU not detected

Problem: nvidia-smi shows no GPUsSolution:

# Check drivers
nvidia-smi

# Reinstall CUDA toolkit if needed
sudo apt update
sudo apt install nvidia-driver-525

# Reboot instance
sudo reboot

Still need help? Our support team is available 24/7. Email [email protected] or click the chat widget for live assistance.

Best Practices

Optimize Usage

Monitor GPU utilization to ensure you’re getting the most from your H100/H200 instances.

Save Checkpoints

Regularly save model checkpoints and important data to persistent storage or cloud.

Monitor Usage

Use nvidia-smi and billing dashboard to track GPU utilization and costs.

Terminate When Done

Always terminate instances when finished to avoid unnecessary charges.

Next Steps

Managing Instances

Learn advanced instance management, monitoring, and automation

API & Automation

Automate deployments with our REST API

Storage & Networking

Configure persistent storage and custom networking

Need Help?

For additional support:

Email: [email protected]
Live Chat: Use the Intercom widget in the bottom-right corner
Documentation: API Reference

Overview

On-Demand GPU

Serverless Inference

Reserved Clusters

General Platform

​Prerequisites

SSH Key Pair

Terminal Access

​Step 1: Create Your Account

​Step 2: Generate & Add SSH Key

​Generate a new SSH key

​Copy your public key

​Using PowerShell

​Using PuTTYgen

​Add SSH Key to Hyperbolic

​Step 3: Fund Your Account

​Step 4: Choose Your GPU

​GPU Selection Guide

Browse On-Demand GPUs

​Step 5: Launch Your First Instance

​Quick Launch (Web UI)

​Step 6: Connect to Your Instance

​First Commands to Run

​Step 7: Start Building

​Quick Examples

​Managing Your Instance

​Monitor Usage

​Save Your Work

​Stop or Terminate

​Common Issues & Solutions

​Connection Issues

​GPU Issues

​Best Practices

Optimize Usage

Save Checkpoints

Monitor Usage

Terminate When Done

​Next Steps

Managing Instances

API & Automation

Storage & Networking

​Need Help?

Prerequisites

Step 1: Create Your Account

Step 2: Generate & Add SSH Key

Generate a new SSH key

Copy your public key

Using PowerShell

Using PuTTYgen

Add SSH Key to Hyperbolic

Step 3: Fund Your Account

Step 4: Choose Your GPU

GPU Selection Guide

Step 5: Launch Your First Instance

Quick Launch (Web UI)

Step 6: Connect to Your Instance

First Commands to Run

Step 7: Start Building

Quick Examples

Managing Your Instance

Monitor Usage

Save Your Work

Stop or Terminate

Common Issues & Solutions

Connection Issues

GPU Issues

Best Practices

Next Steps

Need Help?