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Access the world’s most powerful AI accelerators instantly through Hyperbolic’s On-Demand Cloud platform. Get enterprise-grade H100 and H200 clusters with guaranteed availability and InfiniBand networking.

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Enterprise-Grade GPU Infrastructure

On-Demand Cloud

Professional H100 & H200 GPU ClustersAccess the latest NVIDIA H100 and H200 GPUs, from single instances to massive clusters with InfiniBand networking.
  • Latest Hardware: H100 80GB and H200 141GB GPUs
  • Guaranteed Availability: 99.5% uptime SLA
  • Instant Deployment: Clusters ready in minutes
  • Scalable: Single GPU to 128+ GPU clusters
  • InfiniBand Networking: For maximum multi-GPU performance
  • Flexible Pricing: Pay hourly with no commitments
Perfect for: LLM training, large-scale AI workloads, production inference, and distributed computing

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Key Features

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Instant Deployment

  • Deploy GPUs in under 5 minutes
  • No sales calls or procurement delays
  • Pre-configured with CUDA, PyTorch, TensorFlow
  • Available in multiple regions worldwide

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Flexible Access

  • Full SSH root access to your instances
  • Docker support with pre-built ML images
  • Persistent storage options available (depending on region)

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Simple Billing

  • Pay only for what you use (hourly billing)
  • No upfront commitments or contracts
  • Automatic failure detection (no charges for failed instances)
  • Multiple payment methods: credit card or crypto

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Developer-Friendly

  • REST API for automation
  • Agent-compatible endpoints

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Available Hardware

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Current GPU Offerings

GPU ModelVRAMPrice/HourUse Cases
H100 SXM80GB HBM3$1.39/hrLarge-scale training, LLM fine-tuning, production inference
H100 + InfiniBand80GB HBM3$1.89/hrMulti-node distributed training, massive model parallelism
H200141GB HBM3e$1.99/hrNext-gen AI workloads, ultra-large models, advanced research
More GPUs Coming Soon: We’re expanding our offerings to include Blackwell (B200) chips.

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Regions & Data Centers

GPUs are available across multiple regions for optimal latency and compliance requirements.

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Available Regions

  • North America
  • Europe
  • United Kingdom
Select regions closest to your users for inference workloads, or regions with the best GPU availability for training tasks.

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Use Cases

  • Fine-tune LLMs on custom datasets with multi-GPU support
  • Train computer vision models with high-throughput data pipelines
  • Run distributed training across multiple nodes with InfiniBand
  • Experiment with architectures using automatic checkpointing
Recommended GPUs: H100 for most training tasks, H200 for ultra-large models
  • Host custom inference endpoints with auto-scaling capabilities
  • Deploy production model servers using TorchServe, Triton, or vLLM
  • Run batch inference jobs with optimized throughput
  • A/B test different models with traffic splitting
Recommended GPUs: H100 for high-throughput inference, H200 for serving 70B+ parameter models
  • Prototype AI applications with Jupyter notebooks
  • Test GPU-accelerated code with full debugging capabilities
  • Build ML pipelines with MLflow or Kubeflow integration
  • Reproduce paper results with exact environment replication
Recommended GPUs: H100 for most research, H200 for cutting-edge experiments
  • Large language model training with model parallelism
  • Distributed deep learning with data parallelism
  • High-performance computing workloads
  • Massive batch processing with coordinated jobs
Recommended Setup: 8x or 16x H100 with InfiniBand for optimal performance

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Security Best Practices

Always follow security best practices when deploying GPU instances with public access.

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Instance Security

  • SSH Keys: Use strong SSH keys, never share private keys
  • Updates: Keep your OS and packages updated
  • Monitoring: Set up logging and monitoring for suspicious activity

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Data Protection

  • Encryption: Use encrypted storage for sensitive data
  • Backups: Regular backups of important models and datasets
  • Access Control: Implement proper IAM policies
  • Compliance: Ensure compliance with data regulations (GDPR, HIPAA)

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Performance Benchmarks

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Training Performance Comparison

WorkloadH100 80GBH100 + IB (8x)H200 141GBH200 + IB (8x)
Llama 2 7B Fine-tuning18 min/epoch3 min/epoch15 min/epoch2 min/epoch
Llama 2 70B Fine-tuning3.5 hrs/epoch30 min/epoch2.8 hrs/epoch25 min/epoch
Mixtral 8x7B Training8 hrs1.2 hrs6.5 hrs1 hr
GPT-3 175B Fine-tuningN/A48 hrs36 hrs28 hrs
Benchmarks are approximate and vary based on batch size, precision, and optimization settings.

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Getting Started

1

Set Up Your Account

  • Create your account
  • Add your SSH public key in account settings
  • Fund with $10+ to get started (credit card or crypto)
2

Choose Your GPU

Browse available GPUs at app.hyperbolic.ai
Choose H100 for most AI workloads, or H200 for ultra-large models requiring maximum memory.
3

Launch Instance

  • Select GPU type and quantity
  • Configure storage (if needed)
  • Add or configure your SSH key for access
  • Click “Rent” to deploy
4

Connect & Build

# Connect via SSH
{ssh command from your Active Instances page}

# Verify GPU is available
nvidia-smi

# Start building!
python train.py

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Resources

Quickstart Guide

5-minute tutorial to launch your first GPU instance

API & Automation

Automate deployments with our comprehensive REST API

API Reference

Complete API documentation for all endpoints

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Next Steps

Launch Your First GPU

Ready to deploy? Start with $10 in free credits when you sign up. No credit card required for trial.
Need help? Email [email protected] for support inquiries, or use the in-app chat widget for immediate assistance.