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NVIDIA H100 Price Guide 2024: Detailed Costs, Comparisons & Expert Insights

· 9 min read

Looking for the most accurate and up-to-date NVIDIA H100 GPU pricing information? This guide covers everything from purchase costs to cloud pricing trends in 2024.

Quick Summary of NVIDIA H100 Price Guide 2024:

  • Direct Purchase Cost: Starting at ~$25,000 per GPU; multi-GPU setups can exceed $400,000.
  • Cloud GPU Pricing: Hourly rates range from $2.80 (Jarvislabs) to $9.984 (Baseten).
  • Infrastructure Costs: Consider additional expenses like power, cooling, networking, and racks.
  • Key Choices: PCIe vs SXM versions; choose based on workload, budget, and infrastructure capabilities.
  • Future Trends: Prices are expected to stabilize in 2025 with potential discounts from new GPU releases.

Table of Contents

1. Direct Purchase Cost​

Base Price​

  • Per GPU: Approximately $25,000

Investing in an individual H100 GPU comes with a significant price tag, reflecting its cutting-edge technology and performance.

Full System Cost​

  • Multi-GPU Systems: Up to $400,000

For enterprises requiring multiple GPUs configured in a single system, costs can escalate quickly. These systems are designed for maximum throughput and efficiency but come with a hefty investment.

Infrastructure Costs​

Building a GPU cluster involves substantial additional infrastructure expenses beyond the GPUs themselves:

  • InfiniBand Networking: High-speed interconnects can cost $2,000-$5,000 per node, with switches ranging from $20,000-$100,000 depending on port count and speed.
  • Power Infrastructure: Each H100 GPU requires up to 700W under load. A multi-GPU cluster may need dedicated power distribution units (PDUs) and potentially facility upgrades, adding $10,000-$50,000 to the setup.
  • Cooling Systems: Dense GPU clusters generate significant heat, requiring specialized cooling solutions. Water-cooling infrastructure or enhanced HVAC systems can add $15,000-$100,000 depending on the scale.
  • Rack Infrastructure: Specialized racks, cable management, and monitoring systems add another $5,000-$15,000 per rack.

These infrastructure costs can often match or exceed the cost of the GPUs themselves, making the total investment for a production-ready cluster significantly higher than the base hardware costs.

Volume Discounts​

  • Enterprise Buyers: Volume discounts are often available.

Large-scale purchasers may negotiate better pricing, reducing the per-unit cost when buying in bulk.

2. Cloud GPU Pricing (Per Hour)​

For those who prefer not to invest in physical hardware, cloud-based solutions offer flexible and scalable alternatives. Below is a comparison of hourly rates from various providers:

ProviderPrice Per Hour
Jarvislabs$2.80
Lambda Labs$2.99
Modal$4.56
RunPod$2.99
Baseten$9.984

Cloud services eliminate the need for upfront hardware costs and offer the ability to scale resources based on demand.

3. Cost Considerations​

When evaluating the total cost of using H100 GPUs, consider the following factors:

Cold Start Time​

  • Startup Delays: Some cloud services may have startup times more than 10 minutes. Depending on your use case, this might be a deal-breaker.
  • Impact: Longer startup times can affect productivity and response times in critical applications.

Model Loading Requirements​

  • Memory Constraints: Large models may require significant memory to load.
  • Loading Strategy:
    • Load models once at startup and keep them in memory during the application lifecycle
    • Avoid reloading models between requests to prevent unnecessary memory operations and latency
    • Consider implementing a model caching mechanism for frequently used models
    • For smaller models, loading multiple models simultaneously can be beneficial if:
      • Your system has sufficient memory headroom
      • The use case benefits from parallel processing
      • The combined memory footprint remains manageable
      • You've implemented proper resource monitoring

4. Alternative Options​

Exploring various options can lead to cost savings and performance improvements:

Cloud GPU Platforms​

  • Flexibility: Adjust resources on-demand without long-term commitments.
  • Diversity: A wide range of providers offers different pricing and performance levels.

On-Demand Services​

  • Immediate Access: Quickly spin up instances as needed.
  • Cost-Efficiency: Pay only for the compute time you use.

Enterprise Leasing Programs​

  • Hardware Leasing: Obtain physical GPUs through leasing agreements.
  • Benefits: Lower upfront costs and potential tax advantages.

5. PCIe vs SXM​

When selecting an NVIDIA H100 GPU, it's important to consider the differences between the PCIe and SXM versions, especially for deep learning and AI workloads.

PCIe Version​

  • Compatibility: The PCIe version is compatible with a wide range of systems, making it a flexible choice for various setups.
  • Performance: While still powerful, the PCIe version may not fully utilize the H100's capabilities in high-performance computing environments.
  • Cooling: Typically relies on air cooling, which may not be sufficient for dense configurations.

SXM Version​

  • Performance: The SXM version offers superior performance due to its higher power envelope and NVLink support, which enables faster communication between GPUs.
  • Cooling: Designed for liquid cooling, allowing for more efficient heat dissipation in dense GPU clusters.
  • Deep Learning and AI: The SXM version is particularly beneficial for deep learning and AI workloads due to its enhanced interconnectivity and power efficiency, leading to faster training times and improved throughput.

Choosing the Right Version​

  • Workload Requirements: For intensive AI and deep learning tasks, the SXM version is often the preferred choice due to its performance advantages.
  • Infrastructure: Consider your existing infrastructure and whether it can support the cooling and power requirements of the SXM version.
  • Budget: While the SXM version may have a higher upfront cost, its performance benefits can lead to long-term savings in time and operational efficiency.

By understanding the differences between these versions, you can make an informed decision that aligns with your specific computational needs and budget constraints.

Market Trends & Future Outlook​

Cloud pricing for H100 GPUs has seen significant decreases throughout 2024, with rates dropping from historical highs of $8 per hour to a more competitive range of $2.85-$3.50. This price reduction can be attributed to:

  • Increased market supply
  • More datacenter providers entering the space
  • Improved availability of H100 units
  • Growing competition among cloud providers

2025 Market Projections​

The H100 market is expected to undergo further changes in 2025:

  • New Technology Impact: The anticipated release of the B200 GPU in early 2025 may influence H100 pricing, potentially positioning it as a more cost-effective option for certain applications
  • Price Stability: Despite newer models entering the market, analysts predict relatively stable H100 prices with only minor adjustments (0-5% decreases)
  • Sustained Demand: Enterprise demand remains strong, particularly from companies focused on AI development and cloud service providers

Conclusion​

The NVIDIA H100 GPU represents a significant investment, whether purchasing hardware or utilizing cloud services. By considering all associated costs—including startup times, model requirements, and operational expenses—you can make an informed decision that aligns with your computational needs and budget constraints.

When evaluating providers, it's essential to look beyond just the hourly rates. Assess the total value offered, including performance, support, and any additional features that may benefit your projects.

Frequently Asked Questions (FAQ)​

How much does the NVIDIA H100 GPU cost?​

The NVIDIA H100 GPU costs approximately $25,000 per unit. However, some configurations, such as the NVIDIA H100 80GB GPU, can be priced as high as $30,970.79. The total cost can escalate depending on system setups, additional infrastructure, and networking requirements.

Why is the NVIDIA H100 so expensive?​

The high price of the NVIDIA H100 GPU is due to its cutting-edge architecture, exceptional performance for AI and deep learning workloads, and the limited production capacity of fabs. Additionally, the growing demand for GPUs in data centers and AI research has further driven up the costs.

How much is the NVIDIA H100 in dollars?​

The NVIDIA H100's price in dollars varies:

  • Base Price: ~$25,000
  • Advanced Configurations (e.g., H100 80GB): ~$30,970.79 The price depends on the model, memory size, and vendor-specific markup.

How many NVIDIA H100 GPUs does Tesla use?​

Tesla is reported to have deployed around 35,000 NVIDIA H100 GPUs in its private cloud. This makes Tesla one of the largest adopters of NVIDIA H100 GPUs, primarily for AI and self-driving research.

What are the alternatives to the NVIDIA H100 GPU?​

Some alternatives to the NVIDIA H100 GPU include:

  • NVIDIA A100: A more cost-effective option with slightly lower performance.
  • AMD Instinct MI250: AMD's competitor in the AI GPU market.
  • Google TPU v4: A cloud-based alternative optimized for TensorFlow workloads.

Can I lease an NVIDIA H100 GPU?​

Yes, leasing is a popular option for enterprises. Many providers, such as Jarvislabs, Lambda Labs, and RunPod, offer NVIDIA H100 GPUs on a pay-as-you-go basis. This approach eliminates upfront hardware costs while allowing flexible scaling.

What is the power consumption of the NVIDIA H100 GPU?​

The NVIDIA H100 GPU requires up to 700W of power under full load. For multi-GPU setups, additional power distribution and cooling infrastructure are necessary to support the high energy requirements.

Is the NVIDIA H100 suitable for gaming?​

The NVIDIA H100 is not designed for gaming. It is built for data centers and AI workloads, offering unmatched performance in tasks like deep learning model training and complex simulations. For gaming, GPUs like the NVIDIA GeForce RTX 4090 are more appropriate.

How does the NVIDIA H100 compare to the A100?​

While both GPUs are designed for high-performance computing and AI workloads, the H100 outperforms the A100 in nearly all aspects:

  • Performance: The H100 offers up to 4x the performance of the A100 in specific workloads.
  • Memory: The H100 features higher bandwidth and improved efficiency.
  • Architecture: The H100 is based on NVIDIA’s Hopper architecture, compared to the Ampere architecture of the A100.