What is the difference between H100 and H200?

In the rapidly evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), NVIDIA's GPUs have consistently set the benchmark for performance and efficiency. The introduction of the H100 GPU marked a significant advancement, and with the subsequent release of the H200, NVIDIA has further solidified its leadership in this domain. This article delves into the distinctions between the H100 and H200 GPUs, highlighting their specifications, performance metrics, and practical applications.​

The demand for more powerful and efficient computing solutions has intensified with the growing complexity of AI models and data-intensive tasks. NVIDIA's response to this demand has been the development of the Hopper architecture, which underpins both the H100 and H200 GPUs. While the H100 set new standards upon its release, the H200 introduces enhancements aimed at addressing the escalating requirements of modern computing workloads.​

Memory and Bandwidth

One of the most notable upgrades in the H200 is its memory capacity and bandwidth. The H100 is equipped with 80 GB of HBM3 memory, delivering a bandwidth of 3.35 TB/s. In contrast, the H200 boasts 141 GB of HBM3e memory with a bandwidth of 4.8 TB/s. This substantial increase allows the H200 to handle larger datasets more efficiently, reducing the need to rely on slower system memory and thereby accelerating data processing tasks.

Computational Performance

In terms of raw computational power, both GPUs offer impressive capabilities. The H100 delivers up to 3,958 TFLOPS in FP8 tensor performance, with similar figures across FP16 and FP64 precisions. The H200 maintains these performance levels but leverages its enhanced memory and bandwidth to achieve real-world performance gains. For instance, NVIDIA reports that the H200 achieves up to 45% better performance in generative AI and HPC benchmarks compared to the H100.

Energy Efficiency

Energy consumption is a critical consideration in data centers and large-scale computing environments. Both the H100 and H200 have a power consumption of around 700W. However, the H200 demonstrates improved energy efficiency, using up to 50% less energy per inference workload for large language models (LLMs) compared to the H100. This reduction in energy usage translates to a lower total cost of ownership (TCO) over the GPU's lifespan, making the H200 a more cost-effective choice for continuous, large-scale operations. ​

Architectural Enhancements

Both GPUs are built on NVIDIA's Hopper architecture, introducing features like the Transformer Engine for AI acceleration and Multi-Instance GPU (MIG) for partitioning. The H200 builds upon this foundation by incorporating HBM3e memory, which enhances memory speed and capacity. Additionally, the H200's optimized Tensor Cores and Transformer Engine offer better handling of larger, more complex AI models, ensuring compatibility with newer AI frameworks and distributed training setups. 

Practical Applications

The enhancements in the H200 make it particularly well-suited for next-generation AI and HPC applications hosting. Its increased memory and bandwidth facilitate faster inference on large language models, such as Llama 2 70B, achieving up to twice the speed of the H100. In HPC scenarios, the H200 processes large datasets more efficiently, offering up to 1.7 times the performance of the H100 in mixed HPC workloads. These improvements make the H200 an ideal choice for tasks involving complex simulations, data analysis, and real-time AI inference. 

Availability and Cost

The H100 was released in 2022 and is widely available, with pricing starting around $29,000. The H200, announced in November 2023 and shipping began in Q2 2024, is expected to carry a premium price due to its enhanced features. Exact costs vary by vendor and configuration, but organizations should anticipate a higher investment for the H200's advanced capabilities. 

Conclusion

The NVIDIA H200 represents a significant evolution over the H100, offering substantial improvements in memory capacity, bandwidth, and energy efficiency. These enhancements make it particularly suited for handling the demands of modern AI and HPC workloads. While the H100 remains a powerful and reliable GPU, the H200's advancements provide organizations with a future-proof solution capable of tackling the complexities of next-generation computing tasks.​