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Hyperbolic On-Demand GPUs provide high-performance GPU capacity for training, fine-tuning, inference, benchmarking, and custom deployments. Teams can launch GPU instances, configure their runtime environment, and scale compute as workload requirements change, without committing to reserved infrastructure before the workload is proven. Available GPU types, regions, cluster configurations, and networking options may vary based on current supply. For specific capacity or configuration requirements, contact sales@hyperbolic.ai.

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On-Demand GPU Infrastructure

On-Demand Cloud

H100, H200 & B200 GPU ClustersAccess H100, H200, and B200 GPUs from single instances to massive clusters with InfiniBand networking.
  • Latest Hardware: H100 80GB, H200 141GB, and B200 192GB GPUs
  • Guaranteed Availability: 99.5% uptime SLA
  • Instant Deployment: Clusters ready in minutes
  • Scalable: Single GPU to 128+ GPU clusters
  • InfiniBand Networking: Up to 3.2 Tb/s for maximum multi-GPU performance

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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)
  • Pay by credit card

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Developer-Friendly

  • REST API for automation
  • Agent-compatible endpoints

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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

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 $5.00+ to get started (credit card)
2

Choose Your GPU

Browse available GPUs at app.hyperbolic.ai
Choose H100 for most AI workloads, H200 for ultra-large models requiring maximum memory, or B200 for frontier-scale training and inference demanding the highest performance.
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

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Resources

Quickstart Guide

5-minute tutorial to launch your first GPU instance
Need help? Email support@hyperbolic.ai for support inquiries, or use the in-app chat widget for immediate assistance.