Hopper versus Blackwell

A comparison between the two latest GPU servers

Executive Summary

NVIDIA, the world's leading GPU design company, is facing a potential issue with its upcoming product line-up. The company's next-generation Blackwell architecture, specifically the B200 GPU, is set to significantly outperform its current flagship product, the H100 GPU.

While this technological advancement is a testament to NVIDIA's rapid innovation and competitive edge, it also poses a risk of cannibalizing the sales and market share of the H100 chip.

The Blackwell Architecture

A Leap Forward NVIDIA's Blackwell architecture marks a significant leap in generative AI and accelerated computing. The B200 GPU, built on this architecture, is expected to deliver unprecedented performance improvements over the H100:

2.5X training improvement 5X inference improvement

208 billion transistors (compared to H100's 80 billion)

20 petaflops of FP4 (compared to H100's 4 petaflops of FP8)

These advancements are driven by the second-generation transformer engine, which supports 4-bit floating point (FP4) precision, doubling the performance and model size capabilities while maintaining accuracy.

Potential Cannibalisation of H100 Sales

The superior performance of the B200 GPU may lead to a cannibalization effect on the H100 chip. Key factors contributing to this risk include:

Rapid Release Cycle

NVIDIA has accelerated its product roadmap, with the B200 expected to be released in early 2025, closely following the H100's lifecycle. This shortened gap between product generations may prompt customers to delay purchases or skip the H100 entirely in favour of the B200.

Performance Gap

The significant performance improvements offered by the B200 may make the H100 appear less attractive to customers, especially those working with large-scale AI models and demanding workloads.

Pricing Strategy

The B200 is rumoured to be priced between $30,000 to $40,000, which is higher than the H100's reported cost of $25,000. However, the price difference may not be sufficient to deter customers from opting for the B200's superior performance.

Market Demand

The rapid growth of generative AI and the increasing prevalence of trillion+ parameter models may drive higher demand for the B200, as it is specifically designed to cater to these advanced requirements.

Mitigating the Risk of Cannibalization

To address the potential cannibalization risk, NVIDIA can consider the following strategies:

Market Segmentation: NVIDIA can target the H100 and B200 GPUs toward different market segments based on their specific needs and budgets. The H100 can be positioned for customers with less demanding workloads or those constrained by cost, while the B200 can be promoted for cutting-edge AI applications and high-performance computing.

Pricing and Packaging: NVIDIA can adjust its pricing strategy to make the H100 more attractive to price-sensitive customers. This could involve offering discounts, bundling the H100 with other products or services, or creating value-added packages.

Continued Support and Optimization: NVIDIA can reassure H100 customers by providing ongoing support, software optimizations, and tools to maximize the performance and value of their investments. This can help maintain customer loyalty and prevent premature abandonment of the H100.

Gradual Transition: NVIDIA can manage the transition from H100 to B200 by carefully controlling the supply and availability of both products. This can help prevent a sudden shift in demand and allow for a more gradual adoption of the B200.

Conclusion

The impending release of NVIDIA's B200 GPU, with its superior performance compared to the H100, presents both an opportunity and a challenge for the company. While the B200 showcases NVIDIA's technological leadership and positions the company to capitalize on the growing demand for advanced AI capabilities, it also risks cannibalizing the sales and market share of the H100.

To navigate this issue, NVIDIA must carefully strategize its product positioning, pricing, and support to ensure that both the H100 and B200 can coexist in the market and cater to different customer segments. By effectively managing the transition and highlighting the unique value propositions of each product, NVIDIA can mitigate the risk of cannibalization and maintain its dominant position in the AI accelerator market.

As the AI landscape continues to evolve rapidly, NVIDIA's ability to innovate and adapt will be crucial to its long-term success. The company's proactive approach to addressing the potential cannibalization risk will be a key factor in determining its ability to navigate this challenge and emerge as a leader in the era of generative AI and trillion+ parameter models.

NVIDIA DGX B200

The NVIDIA DGX B200 is a robust and versatile platform designed to meet the demanding requirements of modern AI applications.

Its advanced GPU architecture, high memory bandwidth, and comprehensive networking capabilities make it an ideal choice for enterprises seeking to leverage AI for various applications, from generative AI to large-scale data analytics and beyond. The integration of reliability, security features, and specialized engines ensures that it can support critical business operations efficiently and securely.

Key Features

1. Eight NVIDIA Blackwell GPUs: The DGX B200 is built with eight NVIDIA Blackwell GPUs, which are the latest advancements in GPU technology

2. GPU Memory: It provides 1.4 terabytes (TB) of GPU memory, ensuring that large datasets can be processed efficiently.

3. High Memory Bandwidth: The system boasts 64 terabytes per second (TB/s) of HBM3e memory bandwidth and 14.4 TB/s of all-to-all GPU bandwidth, facilitating rapid data transfer and processing.

4. High Performance: With 72 petaFLOPS of training performance and 144 petaFLOPS of inference performance, the DGX B200 delivers top-tier computational power for AI model development and deployment.

5. Dual Intel Xeon Scalable Processors: Equipped with dual 5th generation Intel Xeon Platinum 8570 processors, offering a total of 112 cores and base clock speed of 2.1 GHz, boostable to 4 GHz.

6. Extensive System Memory: The system includes 2TB of system memory, configurable up to 4TB, providing ample capacity for complex AI tasks.

7. Advanced Networking: Features four OSFP ports and dual-port QSFP112 NVIDIA BlueField-3 DPUs, supporting up to 400Gb/s InfiniBand/Ethernet, ensuring high-speed data communication.

Scalability and Integration

The DGX B200 is designed to integrate seamlessly into NVIDIA DGX BasePOD and NVIDIA DGX SuperPOD architectures, enabling high-speed scalability and making it a turnkey solution for enterprise AI infrastructure.

NVIDIA DGX H100

NVIDIA DGX H100 The NVIDIA DGX H100 is a powerful and comprehensive AI infrastructure solution designed to accelerate business innovation and optimization.

As the latest iteration of NVIDIA's DGX systems, it leverages the groundbreaking performance of the NVIDIA H100 Tensor Core GPU to tackle the most complex AI workloads.

The DGX H100 offers a highly refined, systemized, and scalable platform for enterprises to achieve breakthroughs in various domains, including natural language processing, recommender systems, and data analytics.

Key Features

1. Eight NVIDIA H100 Tensor Core GPUs: The DGX H100 is equipped with eight NVIDIA H100 Tensor Core GPUs, providing cutting-edge performance for AI workloads.

2. GPU Memory: It offers a total of 640GB of GPU memory, enabling efficient processing of large datasets.

3. High Performance: With 32 petaFLOPS of FP8 performance, the DGX H100 delivers exceptional computational power for AI model training and inference.

4. NVIDIA NVSwitch: The system features 4x NVIDIA NVSwitch interconnects, enabling high-speed communication between GPUs.

5. Dual Intel Xeon Platinum Processors: Equipped with dual Intel Xeon Platinum 8480C processors, offering a total of 112 cores with a base clock speed of 2.00 GHz and a max boost of 3.80 GHz.

6. System Memory: The DGX H100 includes 2TB of system memory, providing ample capacity for demanding AI tasks.

7. Advanced Networking: Features eight single-port NVIDIA ConnectX-7 VPI cards and two dual-port QSFP112 NVIDIA ConnectX-7 VPI cards, supporting up to 400Gb/s InfiniBand/Ethernet for high-speed data communication.

The DGX H100 is designed to be the cornerstone of an enterprise AI centre of excellence. It offers a fully optimised hardware and software platform, including support for NVIDIA AI software solutions, a rich ecosystem of third-party tools, and access to expert advice from NVIDIA professional services.

With proven reliability and widespread adoption across various industries, the DGX H100 enables businesses to confidently deploy and scale their AI initiatives.

Scalability and Performance

he DGX H100 breaks through the barriers of AI scalability and performance. With its next-generation architecture, it delivers 9x more performance compared to its predecessor and features 2x faster networking with NVIDIA ConnectX-7 smart network interface cards (SmartNICs).

The system is supercharged for the largest and most complex AI jobs, including generative AI, natural language processing, and deep learning recommendation models.

Software and Management

NVIDIA Base Command powers the DGX H100, providing a comprehensive software suite for AI workload management and optimisation.

Key Differences and Applications

The NVIDIA DGX B200 and DGX H100 are both powerful AI systems, but they have some key differences that make them suitable for different applications:

GPU Architecture

The DGX B200 uses the latest NVIDIA Blackwell GPUs, while the DGX H100 uses the NVIDIA H100 Tensor Core GPUs.

The Blackwell GPUs offer higher performance and memory capacity, making the DGX B200 more suitable for extremely large and complex AI models, such as those used in advanced natural language processing, large-scale recommendation systems, and multi-modal learning.

Performance

The DGX B200 offers significantly higher performance for FP8 training (72 petaFLOPS vs. 32 petaFLOPS) and FP4 inference (144 petaFLOPS vs. 64 petaFLOPS) compared to the DGX H100. This makes the DGX B200 more suitable for organizations dealing with massive datasets and models that require the highest levels of performance.

Memory Capacity

The DGX B200 has more than twice the GPU memory capacity (1,440GB vs. 640GB) compared to the DGX H100. This extra memory allows the DGX B200 to handle larger models and datasets without running into memory constraints, making it ideal for memory-intensive applications like high-resolution image and video processing, 3D modeling, and scientific simulations.

Power Consumption

The DGX B200 has a higher maximum power consumption (14.3kW vs. 10.2kW) compared to the DGX H100. This means that the DGX B200 may require more advanced cooling infrastructure and power management, making it more suitable for organizations with well-equipped data centers and a focus on high-performance computing.

Power Efficiency and Cost Considerations

The NVIDIA DGX B200 has a higher power consumption compared to the DGX H100, with a maximum power usage of approximately 14.3kW, while the DGX H100 has a maximum power usage of 10.2kW. However, it's essential to consider the power efficiency in terms of performance per watt.

Given the DGX B200's significantly higher performance figures, particularly in terms of FP8 training (72 petaFLOPS) and FP4 inference (144 petaFLOPS), it is likely to offer better performance per watt compared to the DGX H100.

TCO HGX H100 vs GB200 NVL72 Source: NVIDIA

The advanced architecture of the NVIDIA Blackwell GPUs, coupled with the increased GPU memory and memory bandwidth, contributes to the improved power efficiency.

By delivering higher performance within a similar power envelope, the DGX B200 can potentially reduce the overall energy consumption and operating costs for AI workloads, especially when considering the faster training and inference times.

Cost Considerations

If the costs of the DGX B200 and DGX H100 were very similar, it would change the analysis in the following ways:

Performance-to-Cost Ratio

With similar costs, the DGX B200's higher performance and memory capacity would make it a more attractive option for organizations looking to maximize their AI capabilities per dollar spent. The DGX B200 would offer better value for money in terms of raw performance and the ability to handle larger and more complex workloads.

Future-Proofing

Investing in the DGX B200, with its more advanced GPU architecture and higher performance, could be seen as a way to future-proof an organization's AI infrastructure. As AI models and datasets continue to grow in size and complexity, the DGX B200's capabilities would allow organizations to stay ahead of the curve and handle evolving workloads more effectively.

Power Efficiency

However, the DGX B200's higher power consumption would still need to be considered, even with similar costs. Organizations would need to assess their power and cooling infrastructure and determine if they can accommodate the DGX B200's power requirements. If power efficiency is a top priority, the DGX H100 may still be the preferred choice.

Specific Use Cases

The choice between the DGX B200 and DGX H100 would also depend on the specific AI applications and workloads of an organization. If an organization's workloads do not require the highest levels of performance or memory capacity offered by the DGX B200, the DGX H100 could still be a suitable and cost-effective option.

In summary, if the costs of the DGX B200 and DGX H100 were similar, the DGX B200 would likely be the more compelling option for organizations prioritizing performance, memory capacity, and future-proofing their AI infrastructure. However, power efficiency and specific use case requirements would still need to be carefully considered when making a decision between the two systems.

Cooling and Data Centre Infrastructure

Given the high power consumption of the DGX B200, it's crucial to consider the cooling requirements and data centre infrastructure necessary to support its operation. Adequate cooling systems and power provisioning should be in place to ensure optimal performance and system stability.

Organizations should assess their existing data centre infrastructure and determine if upgrades or modifications are needed to accommodate the DGX B200's power and cooling demands. This may involve additional investments in cooling equipment, power distribution units (PDUs), and rack space.

Inference: Performance of B200 versus H-100

8x eight-way DGX H100 GPUs air-cooled vs. 1x eight-way DGX B200 air-cooled, per GPU performance comparison

Projected performance subject to change.

1. Token-to-token latency (TTL) = 50ms real time

2. First token latency (FTL) = 5,000ms

3. Input sequence length = 32,768

4. Output sequence length = 1,028

Key Terms and Metrics

1. Token-to-Token Latency (TTL): This is the time it takes for the system to generate each subsequent token (word, subword, or character) in a sequence once the initial token has been produced. In this case, the TTL is 50 milliseconds (ms).

2. First Token Latency (FTL): This is the time it takes for the system to produce the first token in the sequence. For the comparison, the FTL is 5,000 milliseconds (5 seconds). This latency is typically higher due to the initial processing required to start generating text.

3. Input Sequence Length: This refers to the length of the input data sequence fed into the model. Here, the input sequence length is 32,768 tokens.

4. Output Sequence Length: This is the length of the sequence that the model generates as output. In this case, the output sequence length is 1,028 tokens.

5. 8x Eight-Way DGX H100 GPUs Air-Cooled vs. 1x Eight-Way DGX B200 Air-Cooled: This compares the performance of a setup with eight DGX H100 systems, each configured with eight GPUs, against a single DGX B200 system configured with eight GPUs. Both setups are air-cooled.

Speed - Context and Comparison

Token-to-Token Latency (TTL)

• 50ms TTL: This indicates that once the initial token is generated, each subsequent token can be generated every 50 milliseconds.

  • Context:

    • Human Conversation Speed: In human conversation, a typical response time is around 200 milliseconds. Thus, a 50ms TTL is significantly faster, enabling highly responsive interactions that feel instantaneous to users.

    • Typing Speed: A proficient typist averages around 60 words per minute, translating to roughly one word per second. With a 50ms TTL, the system can generate up to 20 tokens per second, outpacing human typing speed and supporting applications like real-time transcription and live chatbots.

    • Gaming and Interactive Media: In fast-paced online gaming, latency of 100ms or less is considered excellent for seamless interaction. A 50ms TTL ensures AI-driven game elements (like NPC responses or dynamic content generation) can keep up with real-time player actions.

First Token Latency (FTL)

• 5,000ms FTL: This shows the initial delay before the system starts producing tokens, which is relatively high but typical for large-scale models due to the extensive computations involved in processing the initial input.

  • Context:

    • Complex Model Initialization: Large language models, like those used in natural language processing and understanding, require significant computation to process the initial input and generate the first token. An FTL of 5,000ms reflects the complexity and depth of these models.

    • Batch Processing in AI: In many AI applications, initial latency can be mitigated by processing data in batches. For instance, in customer service automation, the system can preprocess common queries during off-peak times, reducing perceived latency for users.

    • Video Streaming and Buffering: Similar to buffering in video streaming, the initial load time (FTL) ensures smooth performance during the interaction. Once the first token is produced, the subsequent tokens follow rapidly, akin to continuous streaming without interruptions.

Summary

• Real-Time Applications: A TTL of 50ms is impressively fast and suitable for real-time applications where responsiveness is critical, such as virtual assistants, live customer support, and interactive gaming.

• Initial Processing Delay: While a 5,000ms FTL may seem lengthy, it is a trade-off for the high complexity and capability of large-scale AI models, and strategies like batch processing can help mitigate its impact on user experience.

Overall, these performance metrics highlight the advanced capabilities of the NVIDIA DGX B200 system, making it highly effective for demanding AI applications that require both high-speed processing and the ability to handle complex, large-scale models.

Performance Implications

• High Throughput: The comparison implies that the DGX B200, with its advanced GPU architecture and high memory bandwidth, can significantly outperform the DGX H100 systems in terms of throughput and latency, particularly for large-scale AI tasks.

• Efficiency: By achieving lower latencies and higher throughput, the DGX B200 can handle more complex models and larger datasets more efficiently, reducing the time to insight and accelerating AI deployment in enterprise environments.

Training: Performance of B200 versus H-100

Projected performance subject to change. 32,768 GPU scale, 4,096x eight-way DGX H100 air-cooled cluster: 400G IB network, 4,096x 8-way DGX B200 air-cooled cluster: 400G IB network.

1. 32,768 GPU Scale: This denotes the total number of GPUs involved in each cluster setup. Both clusters are scaled to utilize a total of 32,768 GPUs.

2. 4,096x Eight-Way DGX H100 Air-Cooled Cluster: This cluster configuration consists of 4,096 individual DGX H100 units. Each DGX H100 unit is equipped with eight GPUs, and the entire setup is air-cooled.

3. 4,096x Eight-Way DGX B200 Air-Cooled Cluster: Similarly, this cluster configuration also consists of 4,096 individual DGX B200 units. Each DGX B200 unit is equipped with eight GPUs, and the entire setup is air-cooled.

4. 400G IB Network: Both clusters utilize a 400 gigabits per second (Gbps) InfiniBand (IB) network. InfiniBand is a high-speed networking standard commonly used in high-performance computing (HPC) and AI applications for its low latency and high throughput capabilities.

Comparative Analysis

• Scale and Configuration: Both clusters are designed to scale up to 32,768 GPUs, which indicates a massive computing infrastructure. This level of scaling is typically used for very large and complex AI workloads, such as training massive deep learning models or running extensive simulations.

• Networking: The use of a 400G InfiniBand network in both clusters ensures that data can be transferred between GPUs and across the entire cluster with minimal latency and high bandwidth. This is crucial for maintaining performance and efficiency in distributed computing tasks.

• Cooling: Both clusters are air-cooled, which is an important consideration for maintaining the operational efficiency and longevity of the hardware components. Air cooling is a common method for dissipating heat generated by high-performance computing systems.

Performance Implications

• High Throughput and Low Latency: The combination of a large number of GPUs and high-speed networking implies that both clusters can handle extremely high throughput and low latency, making them suitable for the most demanding AI and HPC tasks.

• Scalability: The ability to scale up to 32,768 GPUs means these clusters can support very large datasets and complex models, providing enterprises with the computational power needed to tackle cutting-edge AI research and applications.

• Advanced AI Capabilities: Given the advanced architecture of the DGX B200 compared to the DGX H100, the B200 cluster is likely to offer superior performance, especially in terms of training and inference speed for AI models. This can lead to faster insights and more efficient use of computational resources.

Conclusion

This projected performance comparison highlights the capabilities of two large-scale GPU clusters configured with NVIDIA DGX H100 and DGX B200 units, respectively.

Both clusters are designed to operate at a massive scale with high-speed InfiniBand networking, providing the computational power and efficiency needed for the most demanding AI and HPC workloads.

The comparison underscores the potential performance improvements offered by the DGX B200 cluster over the DGX H100, positioning it as a more advanced solution for enterprises looking to leverage cutting-edge AI technologies.

Base Command

NVIDIA Base Command is a software suite that enables organisations to fully utilise and manage their NVIDIA DGX infrastructure for AI workloads.

It provides a range of capabilities to streamline the development, deployment, and management of AI applications. Here's a breakdown of the key components and features of NVIDIA Base Command:

Operating System: Provides DGX OS extensions for Linux distributions, optimising the operating system for AI workloads.

Cluster Management: Offers tools for provisioning, monitoring, clustering, and managing DGX systems. Enables efficient management and scaling of DGX infrastructure from a single node to thousands of nodes.

Network/Storage Acceleration Libraries & Management: Includes libraries for accelerating network I/O, storage I/O, and in-network compute. Provides management capabilities for optimizing end-to-end infrastructure performance.

Job Scheduling & Orchestration: Supports popular job scheduling and orchestration frameworks like Kubernetes and SLURM. Ensures hassle-free execution of AI workloads and efficient utilization of resources.

Integration with NVIDIA AI Enterprise: NVIDIA Base Command integrates with NVIDIA AI Enterprise, a suite of software optimized for AI development and deployment. Provides a comprehensive set of AI frameworks, tools, and libraries to accelerate AI workflows.

Ecosystem Integration: NVIDIA Base Command seamlessly integrates with the NVIDIA DGX infrastructure, including DGX systems, DGX BasePOD, and DGX SuperPOD. Supports a wide range of AI and data science tools and frameworks, such as NVIDIA RAPIDS, NVIDIA TAO Toolkit, NVIDIA TensorRT, and NVIDIA Triton Inference Server.

Enterprise-Grade Support: NVIDIA Base Command is fully supported by NVIDIA, providing enterprises with ready-to-use software that speeds up developer success. Offers features to maximize system uptime, security, and reliability.

By leveraging NVIDIA Base Command, organizations can unleash the full potential of their DGX infrastructure, accelerating AI workloads, simplifying management, and ensuring seamless scalability. It provides a comprehensive software stack that abstracts away the complexities of AI infrastructure, allowing developers and data scientists to focus on building and deploying AI applications efficiently.

The combination of NVIDIA Base Command and the DGX infrastructure enables enterprises to establish a robust and scalable AI platform, driving innovation and accelerating time-to-market for AI-powered solutions.

Conclusion

When considering the power efficiency and cost implications of the NVIDIA DGX B200, it's essential to evaluate the performance per watt, total cost of ownership, and long-term business objectives.

While the DGX B200 may have a higher power consumption and initial acquisition cost compared to the DGX H100, its advanced capabilities and efficiency can lead to cost savings and improved productivity in the long run.

Organizations should conduct a thorough analysis of their specific requirements, existing infrastructure, and future scalability needs to determine the most suitable AI infrastructure solution.

The DGX B200's powerful performance, comprehensive software stack, and scalability options make it a compelling choice for enterprises looking to futureproof their AI initiatives and achieve a competitive edge in the rapidly evolving AI landscape.

Performance Table

Here is a comparison table of the key operating and performance metrics for the NVIDIA DGX B200 and NVIDIA DGX H100:

Specification	NVIDIA DGX B200	NVIDIA DGX H100
GPU	8x NVIDIA Blackwell GPUs	8x NVIDIA H100 Tensor Core GPUs
GPU Memory	1,440GB total	640GB total
GPU Memory Bandwidth	64TB/s HBM3e bandwidth	-
Performance (FP8 training)	72 petaFLOPS	32 petaFLOPS
Performance (FP4 inference)	144 petaFLOPS	-
NVIDIA NVSwitch	2x	4x
NVIDIA NVLink Bandwidth	14.4 TB/s aggregate bandwidth	-
System Power Usage	~14.3kW max	10.2kW max
CPU	2 Intel Xeon Platinum 8570, 112 cores	Dual Intel Xeon Platinum 8480C, 112 cores
System Memory	2TB, configurable to 4TB	2TB
Networking	4x OSFP ports, 8x single-port NVIDIA ConnectX-7 VPI, up to 400Gb/s InfiniBand/Ethernet	4x OSFP ports, 8x single-port NVIDIA ConnectX-7 VPI, up to 400Gb/s InfiniBand/Ethernet
	2x dual-port QSFP112 NVIDIA BlueField-3 DPU, up to 400Gb/s InfiniBand/Ethernet	2x dual-port QSFP112 NVIDIA ConnectX-7 VPI, up to 400Gb/s InfiniBand/Ethernet
Storage	OS: 2x 1.9TB NVMe M.2	OS: 2x 1.92TB NVMe M.2
	Internal: 8x 3.84TB NVMe U.2	Internal: 8x 3.84TB NVMe U.2
Software	NVIDIA AI Enterprise, NVIDIA Base Command, DGX OS / Ubuntu	NVIDIA AI Enterprise, NVIDIA Base Command, DGX OS / Ubuntu / Red Hat Enterprise Linux / Rocky
System Dimensions	10 RU, H: 17.5in, W: 19.0in, L: 35.3in	H: 14.0in, W: 19.0in, L: 35.3in
Operating Temperature	5–30°C (41–86°F)	5–30°C (41–86°F)
Enterprise Support	3-year Enterprise Business-Standard Support for hardware and software	3-year business-standard hardware and software support

Key takeaways:

• The DGX B200 uses the latest NVIDIA Blackwell GPUs while the DGX H100 uses NVIDIA H100 Tensor Core GPUs

• The DGX B200 has significantly higher GPU memory (1,440GB vs 640GB) and offers higher performance for FP8 training and FP4 inference

• The DGX B200 has higher max power consumption (14.3kW vs 10.2kW)

• Networking and storage specs are very similar between the two systems

• Both come with comprehensive software stack and 3 years of enterprise support