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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 & B300 GPU ClustersAccess H100, H200, and Blackwell GPUs from single instances to interconnected clusters, plus RTX Pro 6000 for single-GPU inference and development.
  • Hardware: H100 80GB, H200 141GB, B200 192GB, B300 288GB, RTX Pro 6000 96GB
  • 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
  • Ubuntu with NVIDIA drivers and Docker preinstalled — bring your own frameworks or containers
  • Available in multiple regions worldwide

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Flexible Access

  • Full SSH root access to your instances
  • Docker support
  • Persistent storage options available (depending on region)
  • Built-in GPU monitoring on supported instances — utilization, memory, and temperature, no agent to install

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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

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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

Regions currently offered on the platform. GPU availability changes hour to hour — the console shows what’s rentable right now in each region. Regions listed with InfiniBand or RoCE support multi-node clusters; single-GPU (1×) options are noted where offered. Persistent storage volumes are available in select regions — the console shows the storage option for regions that support it. B200 and B300 capacity is rolling out. To get an email when a configuration comes online, create an availability alert.
VM here means full bare-metal passthrough. The GPUs, RDMA NICs, and NVMe drives are handed to your VM directly rather than virtualized, and per-rail bandwidth inside the VM measures at line rate. The console’s type and interconnect filters only list what is rentable at that moment, so if you don’t see bare metal or InfiniBand there, it’s stock, not the catalog.

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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 inference endpoints on your own instances
  • 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

For hardware specifications per GPU type (memory, bandwidth, interconnect), see the GPU comparison table. To confirm an instance is performing as expected, run the benchmark tests in Verifying Instance Performance.

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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

Availability Alerts

Get an email when the GPUs you need are available
Need help? Email support@hyperbolic.ai for support inquiries, or use the chat in the bottom-right corner of the app after you sign in.