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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, B200, or B300 clusters (8, 16, 32, 64, 128+ GPUs)
  • 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; extend before it ends, and Reservation Protection keeps them running at the on-demand rate afterwards
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, B300)
  • Network isolation with private subnets and firewall rules
  • High-bandwidth InfiniBand interconnect for distributed training
  • Choice of operating model: full platform visibility, Managed Kubernetes (beta), or a fully isolated environment where only your team holds credentials. Managed Slurm is on the roadmap.
  • 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, B200, and B300 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 2 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). Node configuration (B200): 8× NVIDIA B200 per node with dual Intel Xeon 6960P CPUs, ~3 TB of RAM, NVMe storage (960 GB OS drive plus 4× 3.84 TB in RAID5), and 2× 100 GbE bonded networking. 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, B200, and B300? 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. Choose B300 when memory per GPU is the constraint — largest models and longest contexts.

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