NVIDIA’s Hopper H200 GPU Excels in Latest MLPerf 4.0 Results, Empowered by TensorRT-LLM
In the fast-paced realm of artificial intelligence, NVIDIA continues to lead the charge with its cutting-edge TensorRT-LLM suite, propelling the H200 GPUs to remarkable heights in the latest MLPerf v4.0 results.
Amidst the competitive landscape of Generative AI (GenAI), NVIDIA remains unrivaled, capturing the lion’s share of the market with its exceptional performance showcased in the MLPerf v4.0 inference results.
Continuous refinement of TensorRT-LLM since its launch last year has led to substantial performance improvements, as evidenced by the significant gains witnessed in the previous MLPerf v3.1 results. With MLPerf v4.0, NVIDIA further elevates the performance of its Hopper GPUs, setting new standards in the industry.
Inference plays a pivotal role, accounting for a significant portion of data center revenue. From Large Language Models (LLMs) to Visual Content and Recommenders, inference workloads demand robust hardware and software solutions to handle increasing complexity and scale.
TensorRT-LLM, a state-of-the-art inference compiler intricately designed with NVIDIA GPU architectures, offers unparalleled efficiency and performance. Featuring advanced features such as In-Flight Sequence Batching and KV Cache Management, TensorRT-LLM optimizes GPU utilization and memory efficiency, catering to diverse inference workloads.
Leveraging the latest optimizations within TensorRT-LLM, NVIDIA achieves remarkable performance gains for its Hopper GPUs in MLPerf v4.0, boasting a 2.9x improvement over its predecessor, MLPerf v3.1. Setting new benchmarks in MLPerf Llama 2, the H200 (Preview) generates up to 31,712 tokens per second, while the H100 reaches 21,806 tokens per second.
Despite being benchmarked just a month ago, NVIDIA has already begun sampling the H200 GPUs to customers, with shipments expected in the second quarter. The H200 GPU outperforms its predecessor, the H100, by 45% in Llama 2, thanks to its enhanced memory configuration and accelerated bandwidth.
In comparison to Intel’s Gaudi 2, the H200 GPU emerges as a formidable competitor within the MLPerf v4.0 benchmarks, while the H100 demonstrates a significant 2.7x performance gain.
Moreover, an 8 GPU NVIDIA HGX H200 GPU system sets records in the Stable Diffusion XL benchmark, achieving outstanding performance metrics in server and offline scenarios.
Beyond standalone performance, the H200 GPU offers drop-in compatibility with H100 platforms and features a custom thermal design variant, the MGX platform, delivering up to 14% higher performance with enhanced TDP.
With Blackwell GPUs on the horizon, NVIDIA anticipates further advancements in MLPerf submissions, promising continued innovation in the AI landscape.
Reserve H100 and H200 (and a thousand more GPUs) here: https://clust.ai/reserve_nvidia_card_gpu/