Nvidia's H200 Outperforms Intel Gaudi2 In AI Inference Benchmarks

Illustration shows NVIDIA logo

Recent benchmarks released by Nvidia showcase their dominance in AI inference performance, particularly with their latest GPUs like Hopper and H200. Nvidia's focus on optimizing inferencing on their GPUs has led to significant performance improvements, with the TensorRT-LLM compiler playing a crucial role in enhancing processing efficiency.

The benchmarks highlight Nvidia's prowess in handling Large Language Models (LLMs), with impressive results on the Llama 2 model from Meta. The company's commitment to advancing inference processing is evident in the nearly three-fold improvement in performance on models like GPT-J over the last six months.

Intel also made strides in AI performance, with their 5th Gen Xeon CPUs showing a 42% increase in AI inference performance compared to the previous generation. While AMD did not submit results, Intel positioned itself as a strong alternative to Nvidia's offerings, particularly for Gen AI workloads.

TensorRT-LLM compiler enhances processing efficiency on Nvidia GPUs.

Nvidia excels in AI inference performance with Hopper and H200 GPUs.

Nvidia demonstrates strength in handling Large Language Models (LLMs).

MLCommons, with over 125 founding member and affiliate companies, continues to drive industry-wide benchmarking efforts. While the lack of submissions from key players like AMD and Google may limit the marketing value of these benchmarks, they serve as a valuable resource for evaluating hardware and software improvements in the AI space.

Overall, the latest benchmarks underscore the ongoing competition and innovation in the AI hardware landscape, with Nvidia and Intel leading the charge in delivering high-performance solutions for inference workloads.