GPUs have become a cornerstone in the field of artificial intelligence, particularly in deep learning applications. Nvidia’s new Hopper GPU architecture is at the frontier of this evolution, boasting features like FP8 tensor cores, DPX, and distributed shared memory.

• Researchers focus on unravelling the performance and operations of Hopper’s new tech.
• The study includes thorough benchmarks comparing with prior GPU architectures: Ampere and Ada.
• Novel Hopper functions like DPX instructions and FP8 tensor cores are benchmarked in detail.
• Results aim to assist in software optimization and harnessing Hopper’s full AI potential.
• This unprecedented research could expand possibilities for AI applications requiring intense computation.

This thorough benchmarking of Hopper GPUs is crucial for optimizing AI models and software. The exploration of its unique features opens doors to enhanced AI performance and efficiency, positioning Nvidia’s latest creation as a significant contribution to AI research and practical applications.