NVIDIA Unveils H100 GPU with Hopper Architecture: Massive Performance Gains for AI

First Hopper Architecture GPU, Massive Performance Boost

NVIDIA announced the H100 GPU, the first product of the Hopper architecture, fabricated on TSMC's 4nm process and integrating 800 billion transistors.

The card packs 16,896 CUDA cores—about 2.5× more than the previous A100—and delivers at least a three‑fold increase in FP32, FP64, INT8, FP16, and TF32 tensor performance.

Its thermal design power reaches an unprecedented 700 W, and it is the first GPU to support PCIe 5.0 and HBM3, achieving a memory bandwidth of 3 TB/s.

H100 also introduces a dedicated Transformer engine that speeds up large‑model training by up to 6×, reducing training time for models like GPT‑3 (175 billion parameters) and a 395 billion‑parameter transformer from weeks to a single day.

Training 395 Billion‑Parameter Model in One Day

The new Transformer engine enables training of a 395 billion‑parameter model in roughly 21 hours, a nine‑fold speedup over previous generations, and improves inference throughput for massive models such as the 530 billion‑parameter Megatron by 30×.

H100 also adds fourth‑generation NVLink, boosting inter‑GPU bandwidth to 900 GB/s, and introduces security features like instance isolation and confidential computing.

4608 H100s Build World's Fastest AI Supercomputer

NVIDIA's DGX H100 system, equipped with eight H100 GPUs, delivers 32 Petaflops of AI performance at FP8 precision—six times faster than the DGX A100.

Using 4,608 H100 GPUs, NVIDIA assembled the Eos supercomputer, achieving 18.4 Exaflops of AI compute, roughly four times faster than Japan's Fugaku, and 275 Petaflops for traditional scientific workloads.

Assembling CPUs, Benchmark Leads

The Grace CPU Superchip combines two Grace CPUs (Arm v9, 144 cores) with up to 1 TB/s memory bandwidth, offering double the bandwidth of previous designs while consuming only about 500 W.

In SPECint 2017 benchmarks, the Grace Superchip scores 740 points—1.5× the performance of the CPUs in a DGX A100.

NVIDIA also opened its NVLink‑C2C interconnect to third‑party custom chips, enabling ultra‑fast chip‑to‑chip communication across GPUs, CPUs, DPUs, NICs, and SoCs.

Industrial Metaverse Applications

NVIDIA highlighted industrial use cases such as autonomous‑driving simulation and digital twins, where AI models can be trained in virtual environments to increase data diversity.

The company showcased Omniverse Cloud for collaborative 3D work and demonstrated AI‑driven virtual characters that can be trained in days of real‑world time but acquire years of skill through reinforcement learning.

These developments aim to give the metaverse practical relevance by providing high‑fidelity simulation, training, and content creation capabilities.