H100 GPU Pricing: What You Need To Know in 2023

What is the NVIDIA H100 GPU?

The NVIDIA H100 Tensor Core GPU (also known as Hopper) represents NVIDIA's most advanced data center GPU architecture designed specifically for accelerating AI and high-performance computing workloads. Built on NVIDIA's Hopper architecture, the H100 delivers exceptional performance for training and inference across various AI models, particularly large language models that power today's generative AI applications.

The H100 features fourth-generation Tensor Cores, significantly improved performance over previous generations, and specialized hardware for accelerating transformers - the neural network architecture behind many breakthrough AI models. With 80GB of high-bandwidth memory and unprecedented computational throughput, the H100 has quickly become the gold standard for organizations implementing enterprise AI solutions.

H100 Pricing Models and Availability

The pricing structure for NVIDIA H100 GPUs varies significantly based on several factors, including deployment model, quantity, and vendor relationships. As a highly sought-after component, H100 GPUs typically command premium pricing reflecting their computational capabilities and market demand.

For on-premises deployments, organizations can expect to invest approximately $25,000 to $40,000 per individual H100 PCIe card when purchasing directly from hardware vendors. Server configurations featuring multiple H100 GPUs can range from $100,000 to over $500,000 depending on the number of GPUs and supporting infrastructure. For many enterprises, the substantial capital expenditure required for direct purchase has driven interest in cloud-based alternatives.

It's important to note that actual pricing may fluctuate based on market conditions, supply constraints, and volume discounts negotiated with vendors. Organizations planning H100 deployments should engage directly with authorized NVIDIA partners for current pricing and availability information.

Cloud Provider H100 Pricing Comparison

For many organizations, accessing H100 computing power through cloud providers offers a more flexible alternative to outright purchases. Major cloud platforms have introduced H100-based instances with various pricing structures:

Cloud-based H100 instances typically range from $15 to $40 per hour for a single H100 GPU, with significant discounts available through committed-use contracts. Multi-GPU configurations naturally scale in cost based on the number of accelerators. This approach allows organizations to access H100 capabilities without the substantial upfront investment required for on-premises deployment.

When evaluating cloud offerings, consider factors beyond base pricing, including network performance, storage options, and software ecosystem compatibility. Many providers also offer specialized AI platforms built around H100 infrastructure that may provide additional value beyond raw computing resources.

Total Cost Considerations Beyond Purchase Price

When evaluating H100 investments, organizations must consider several factors beyond the initial hardware or cloud service costs. These additional considerations significantly impact the total cost of ownership:

Power and Cooling: H100 GPUs have a thermal design power (TDP) of approximately 700 watts per card. At scale, this creates substantial power and cooling requirements. Data centers may need infrastructure upgrades to accommodate these demands, with power costs becoming a significant operational expense.

Specialized Infrastructure: Maximizing H100 performance requires complementary high-performance components, including NVLink connections, high-bandwidth networking, and optimized storage solutions. These supporting technologies add to the overall system cost.

Organizations must also account for software licensing, particularly for NVIDIA's enterprise AI frameworks and development tools. While some software is included with hardware purchases, comprehensive enterprise deployments often require additional licenses.

For cloud deployments, data transfer costs between regions or to on-premises environments can quickly accumulate. Careful planning of data workflows is essential to manage these expenses effectively. Additionally, the specialized expertise required to optimize workloads for H100 hardware represents another investment, whether through hiring, training, or consulting services.

ROI and Performance Benefits

Despite the significant investment required, H100 GPUs can deliver compelling return on investment for specific use cases. Organizations should evaluate potential benefits including:

Training Acceleration: The H100 can reduce AI model training times by 3-6x compared to previous generation A100 GPUs. For organizations regularly training large models, this translates to faster innovation cycles and reduced operational costs.

Inference Efficiency: For production AI deployments, H100 GPUs can handle significantly higher inference throughput, potentially reducing the number of servers required to support user-facing applications.

The performance advantages are particularly pronounced for transformer-based models, where the H100's specialized hardware accelerators provide exceptional efficiency. Organizations working with large language models, diffusion models, or other transformer architectures stand to gain the most immediate benefit.

When calculating ROI, organizations should consider both direct cost savings from computational efficiency and indirect benefits such as faster time-to-market for AI initiatives, improved model quality through more extensive experimentation, and competitive advantages gained through access to cutting-edge AI capabilities.

Cloud providers like Lambda Labs and CoreWeave specialize in H100-based infrastructure for AI workloads and may offer more competitive pricing for specific use cases compared to major cloud platforms.

Conclusion

The NVIDIA H100 GPU represents a significant investment for any organization, with pricing structures that vary widely across deployment models and providers. Whether pursuing on-premises deployment or cloud-based access, decision-makers must carefully evaluate their specific computational needs, budget constraints, and long-term AI strategy.

For many organizations, the substantial performance improvements offered by H100 GPUs justify their premium pricing, particularly for advanced AI workloads where computational efficiency directly impacts business outcomes. As market dynamics evolve and more H100 inventory becomes available, pricing structures will likely become more competitive across both purchase and cloud-based options.

Organizations should approach H100 investments with a comprehensive understanding of both direct costs and the broader ecosystem requirements. By carefully matching deployment models to specific use cases and workload patterns, technology leaders can maximize the return on their H100 investments while positioning their AI initiatives for success.

Citations

• https://www.nvidia.com/
• https://aws.amazon.com/
• https://azure.microsoft.com/
• https://cloud.google.com/
• https://www.oracle.com/cloud/
• https://www.lambdalabs.com/
• https://www.coreweave.com/

This content was written by AI and reviewed by a human for quality and compliance.