Graphics Processing Units (GPUs) are critical for AI advancements, particularly in handling complex computations required by AI algorithms. The Nvidia Hopper GPU introduces a range of new features including FP8 tensor cores, DPX, and distributed shared memory. This paper presents a comprehensive benchmarking study:

• Detailed latency and throughput comparisons among recent GPU architectures.
• In-depth examination of Hopper’s new features.

The Hopper architecture supports various AI applications with enhanced efficiency and programmability, suggesting significant implications for AI-driven innovations:

• Extended programming capabilities with the Hopper ISA.
• Advanced memory management for larger, more complex models.

This exploration is pivotal as it lays the foundation for future GPU technologies and their applications in AI. Its potential to drive software optimization and performance improvements is immense, illustrating the continuous need for hardware innovations to keep pace with software advancements.