NVIDIA H200 GPU

NVIDIA H200 GPU

NVIDIA H200 GPU

NVIDIA H200 GPU

Access NVIDIA H200 GPUs across global providers. Compare pricing, locations, configurations, and deployment options without vendor lock-in

Access NVIDIA H200 GPUs across global providers. Compare pricing, locations, configurations, and deployment options without vendor lock-in

CURRENT MARKET RATE

CURRENT MARKET RATE

$1.50 - $2.25

/hr

WHAT IS NVIDIA H200 GPU?

WHAT IS NVIDIA H200 GPU?

The NVIDIA H200 Tensor Core GPU, based on NVIDIA’s Hopper architecture, is designed for large-scale AI workloads that benefit from increased memory capacity and bandwidth, including LLM training, fine-tuning, and high-throughput inference.

Compared to H100, H200 significantly expands available HBM memory and bandwidth, making it particularly well suited for memory-bound workloads such as large transformer models, long-context inference, and data-intensive training pipelines.

H200 GPUs are commonly deployed as reserved capacity by AI startups, research organizations, and enterprises running sustained training or production inference workloads.

NVIDIA H200 SPECIFICATIONS

ARCHITECTURE

NVIDIA Hopper

MEMORY

141 GB HBM3e

MEMORY BANDWIDTH

Up to ~4.8 TB/s

PRECISION SUPPORT

FP8 / FP16 / BF16 / TF32

INTERCONNECT

Up to 900 GB/s NVLink

FORM FACTORS

SXM5

PRIMARY USE CASES

Large language model training, Memory-bound fine-tuning, High-throughput inference

TDP

700W (SXM)

* Exact performance and networking capabilities vary by provider and configuration.

* Exact performance and networking capabilities vary by provider and configuration.

WHAT IS H200 USED FOR?

01

Large language model training

H200 is used to train large transformer-based models where increased memory capacity and bandwidth help reduce communication overhead and improve training efficiency.

01

Large language model training

H200 is used to train large transformer-based models where increased memory capacity and bandwidth help reduce communication overhead and improve training efficiency.

01

Large language model training

H200 is used to train large transformer-based models where increased memory capacity and bandwidth help reduce communication overhead and improve training efficiency.

02

Fine-tuning & continued pre-training

Teams use H200 for fine-tuning and continued pre-training on proprietary or domain-specific datasets, particularly when model size or dataset scale pushes the limits of H100-class memory capacity.

02

Fine-tuning & continued pre-training

Teams use H200 for fine-tuning and continued pre-training on proprietary or domain-specific datasets, particularly when model size or dataset scale pushes the limits of H100-class memory capacity.

02

Fine-tuning & continued pre-training

Teams use H200 for fine-tuning and continued pre-training on proprietary or domain-specific datasets, particularly when model size or dataset scale pushes the limits of H100-class memory capacity.

03

High-throughput inference

For large inference workloads with high concurrency, H200 enables more efficient batching and higher throughput than H100, making it well suited for sustained production inference in reserved clusters.

03

High-throughput inference

For large inference workloads with high concurrency, H200 enables more efficient batching and higher throughput than H100, making it well suited for sustained production inference in reserved clusters.

03

High-throughput inference

For large inference workloads with high concurrency, H200 enables more efficient batching and higher throughput than H100, making it well suited for sustained production inference in reserved clusters.

04

Multi-node distributed workloads

H200 systems are commonly deployed in multi-node clusters, using NVLink for high-speed GPU-to-GPU communication within a node and InfiniBand-based networking across nodes, depending on provider infrastructure.

04

Multi-node distributed workloads

H200 systems are commonly deployed in multi-node clusters, using NVLink for high-speed GPU-to-GPU communication within a node and InfiniBand-based networking across nodes, depending on provider infrastructure.

04

Multi-node distributed workloads

H200 systems are commonly deployed in multi-node clusters, using NVLink for high-speed GPU-to-GPU communication within a node and InfiniBand-based networking across nodes, depending on provider infrastructure.

H200 Pricing Is Not Fixed - It's A Market

H200 Pricing Is Not Fixed - It's A Market

H200 pricing varies based on region, provider, system configuration, networking, and availability. As a newer generation compared to H100, access to H200 capacity is often tied to longer-term commitments and reserved capacity agreements.

Live H100 availability
Regional price differences
Configuration comparisons
Flexible deployment options

Instead of negotiating bilaterally with a single provider, teams can benchmark H200 capacity across the broader market before committing.

Instead of negotiating bilaterally with a single provider, teams can benchmark H200 capacity across the broader market before committing.

DEPLOYMENT OPTIONS

DEPLOYMENT OPTIONS

Reserved H200 capacity across cloud, neocloud, and independent providers

Neocloud providers with optimized AI infrastructure

Contract-based allocations with defined terms and guaranteed availability

Single-node and multi-node cluster reservations

Commitments aligned with sustained training or production inference workloads

"Compute Exchange acts as a broker and marketplace layer, helping buyers match workload needs to the right supply — without forcing architectural changes."

Global Network

Access verified providers across North America, Europe, and Asia Pacific, offering reserved capacity in multiple regions and configurations.

Why Buy H100 Through Compute Exchange

Verified suppliers
Verified suppliers

Access pre-vetted H200 suppliers globally, with capacity validated for reserved deployments.

Access pre-vetted H200 suppliers globally, with capacity validated for reserved deployments.

Transparent comparison
Transparent comparison

Compare reserved H200 capacity across providers, regions, and configurations with clear visibility into technical and commercial tradeoffs.

Compare reserved H200 capacity across providers, regions, and configurations with clear visibility into technical and commercial tradeoffs.

Faster sourcing
Faster sourcing

Reduce procurement timelines by accessing existing reserved capacity and structured contracts rather than negotiating from scratch.

Reduce procurement timelines by accessing existing reserved capacity and structured contracts rather than negotiating from scratch.

Complex requirements
Complex requirements

Support for networking topology, cluster sizing, compliance constraints, and region-specific deployment needs.

Support for networking topology, cluster sizing, compliance constraints, and region-specific deployment needs.

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The NVIDIA GB200 Grace Blackwell Superchip connects two NVIDIA B200 Tensor Core GPUs to the NVIDIA Grace CPU over a high-speed NVLink-C2C interconnect.

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EXCHANGE

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LINKEDIN

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COMPUTE

EXCHANGE

The transparent GPU marketplace for AI infrastructure. Built for builders.

ALL SYSTEMS OPERATIONAL

© 2025 COMPUTE EXCHANGE

TWITTER

LINKEDIN

GITHUB

BUILT FOR THE AI ERA

COMPUTE

EXCHANGE

The transparent GPU marketplace for AI infrastructure. Built for builders.

ALL SYSTEMS OPERATIONAL

© 2025 COMPUTE EXCHANGE

TWITTER

LINKEDIN

GITHUB

BUILT FOR THE AI ERA