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Quick Comparison Table

GPU rates vary by region and availability. Always check app.hyperbolic.ai/gpus for current rates and real-time availability.

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Detailed Comparison

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On-Demand GPUs

Self-serve, pay-as-you-go GPU instances drawn from an aggregated supplier network. Spin up a single GPU or an interconnected multi-node cluster in minutes — no contracts, no upfront commitment, no sales calls.When to use:
  • Training and fine-tuning custom models
  • Running experiments, notebooks, and evaluations
  • Burst and batch processing jobs
  • Short training runs where you need full control of the environment
Key features:
  • Full root access via SSH
  • Virtual Machine or Bare Metal configurations
  • Deploy interconnected H100, H200, or B200 clusters (8, 16, 32, 64, 128+ GPUs) instantly
  • Up to 24 TB local NVMe storage; attachable network storage (~$0.0766/TB/month)
  • Hourly billing with no hidden or egress fees — failed instances are never charged

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Reserved

The same GPU capacity you would use on-demand, reserved for a fixed term at a discounted rate. Same platform, same hardware, same setup — you simply commit up front for a lower price.When to use:
  • 24/7 production inference and LLM tooling
  • Sustained or scheduled training runs
  • High-volume, predictable usage
  • Teams that want a discount on capacity they already run on-demand
Key features:
  • Same on-demand GPUs, networking, and environment — reserved for your term
  • Self-serve in the app; larger commitments can also be arranged with our team
  • Discounted rate in exchange for committing to a term
  • Reserved instances run for the full term and are not terminated early
Pricing structure:
  • Discounted prepaid $/GPU/hour — longer terms unlock lower rates
  • Paid up front for the reservation period
  • See Pricing for details

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Private Cloud

Dedicated, single-tenant GPU infrastructure for production-scale AI workloads. Get an isolated environment with custom networking, storage, and SLAs — plus the best unit economics on long-term commitments.When to use:
  • Enterprise production workloads at sustained scale
  • Security-conscious teams that require single-tenant isolation
  • Large, long-term commitments (typically $1M+ deployments)
  • Workloads that need custom SLAs, custom networking, or custom storage
Key features:
  • Single-tenant, customer-isolated GPU clusters (H100, H200, B200)
  • Network isolation with private subnets and firewall rules
  • High-bandwidth InfiniBand interconnect for distributed training
  • Choice of operating model: full platform visibility, managed Kubernetes, managed Slurm, or a fully isolated environment where only your team holds credentials
  • Custom storage architecture (node-local NVMe and shared/parallel filesystems)
  • Dedicated, direct-to-engineer support with 24×7 coverage for critical issues
Pricing structure:
  • Custom contract based on cluster size, GPU type, term length, and configuration
  • Long-term commitments with the best unit economics
  • Talk to sales for a tailored quote

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Available GPUs

Hyperbolic offers H100, H200, and B200 capacity across On-Demand GPUs, Reserved, and Private Cloud. Availability may vary by product, region, supply, and configuration. If you need a specific GPU type, cluster configuration, or capacity profile, contact sales@hyperbolic.ai and our team can help evaluate what is available or what can be sourced for your workload. Node configuration (H100 / H200): 8 GPUs per node with up to 160 vCPUs and up to 1.5 TB RAM per node, and up to 24 TB of local NVMe storage. Available as Virtual Machines (fast, flexible, ideal for development and small-to-medium training) or Bare Metal (full root access and system-level control, ideal for large-scale distributed training and custom CUDA configurations). Interconnect: Single-node instances use standard networking; multi-node clusters can use InfiniBand (up to 3.2 Tb/s, NDR / ConnectX-7) for low-latency GPU-to-GPU communication — essential for distributed training at 32+ GPUs.
Choosing between H100, H200, and B200? Use H100 for mainstream training, fine-tuning, and inference. Choose H200 for larger models, longer context, or memory-bound inference. Choose B200 for maximum per-GPU throughput on frontier-scale training and inference.

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