NVIDIA H100

Train, fine-tune and serve models on an 80 GB GPU. Start with a GPU environment or plan a reserved cluster for work that spans multiple nodes.

Launch H100Compare GPU pricing

From $2.69/GPU/hour · USD on-demand rate · Storage extra

Workloads

What to run on H100.

Choose for your workload’s memory needs, software support and measured runtime.

Model training

Run CUDA-based training with frameworks such as PyTorch. Choose a batch size that fits weights, gradients and optimizer state.

LLM inference

Allocate memory for model weights and KV cache. Context length and concurrent requests affect the capacity you need.

Multi-node training

Reserve an InfiniBand-connected cluster when your distributed workload needs more than one node.

Choose your setup

Start with the environment you need.

Use a preconfigured container or manage your operating system in a GPU VM. The dashboard shows current GPU and region availability.

GPU Templates

Start with PyTorch, ComfyUI or another supported environment. Drivers and framework dependencies are preconfigured.

Explore templates

GPU VMs

Get SSH and full root access when you need control over the operating system and runtime. Available configurations vary by GPU and region.

Explore VMs

GPU clusters

H100 is available for reserved clusters with InfiniBand networking.

Explore clusters

Sizing

Start with memory. Then measure performance.

Model weights are only part of the working set. Leave room for everything the job needs while it runs.

For inference

Account for model precision, context length, KV cache and concurrent requests. A model loading successfully does not tell you how much traffic it can serve.

For training

Include activations, gradients and optimizer state. Batch size, sequence length and checkpointing change memory use.

For multiple GPUs

GPU memory is not automatically pooled. Use a framework and parallelism strategy that distribute the workload across devices.

Hardware specifications: NVIDIA H100. Software and workload affect realized performance.

Compare options

Compare memory and hourly rates.

Use this as a shortlist, then test your workload. Lower hourly pricing does not always mean a lower total job cost.

Published USD on-demand rates per GPU; storage extra. Availability and regional prices vary.

  • NVIDIA H200
    Memory
    141 GB HBM3e
    Architecture
    Hopper
    From / GPU / hour
    $3.99
  • NVIDIA H100
    Memory
    80 GB HBM3
    Architecture
    Hopper
    From / GPU / hour
    $2.69
  • NVIDIA B200
    Memory
    180 GB per GPU¹
    Architecture
    Blackwell
    From / GPU / hour
    Request a quote
  • NVIDIA RTX PRO 6000
    Memory
    96 GB GDDR7
    Architecture
    Blackwell
    From / GPU / hour
    $1.89
  • NVIDIA A100 80 GB
    Memory
    80 GB HBM2e
    Architecture
    Ampere
    From / GPU / hour
    $1.49
  • NVIDIA A100 40 GB
    Memory
    40 GB HBM2
    Architecture
    Ampere
    From / GPU / hour
    $0.89
  • NVIDIA L4
    Memory
    24 GB GDDR6
    Architecture
    Ada Lovelace
    From / GPU / hour
    $0.44

Same model, same serving stack, both GPUs: Read the measured H100 vs H200 comparison

Before you start

Common questions.

Practical details for choosing and using this product.

How do I get started with NVIDIA H100?

Choose a template or VM, select the GPU and region, and review the configuration and price in the dashboard before launch.

Is storage included in the GPU rate?

Storage is billed separately. Retained storage continues to incur charges while an instance is paused. Review the full configuration price before launching.

Will my model fit on one GPU?

This GPU has 80 GB HBM3. Fit depends on weights, precision, framework overhead and workload state. For inference, also account for context and concurrency; for training, include activations and optimizer state.

Can I get more than eight GPUs?

For workloads spanning nodes, explore GPU clusters. Reserved capacity can be planned at 128, 256, 1,024 GPUs and beyond, subject to configuration and availability.

Get started with NVIDIA H100.

Launch a GPU instance or talk to our team about the right setup for your workload.

Launch H100