Technology, Process and Cost

NVIDIA GPU H200 Hopper

Jun 2025

Detailed physical data, GPU architecture analysis and cost structure of NVIDA’s latest generation of Hopper GPU

SPR25932

Key Features

Detailed optical and SEM photos
Precise measurements
Die analysis
Package analysis
CoWoS manufacturing process flow
Supply chain evaluation
Manufacturing cost analysis
Physical Comparisons
Floorplan

What's new

Latest NVIDIA Hopper GPU generation
Comparison to the NVIDIA’s previous generation of data center GPUs

Product objectives

Exploring the technology behind the latest NVIDIA Hopper GPU generation
Insight into the GPU technology used by leading players
A comprehensive study of the physical structure and detailed data on chip packaging, GPU dies
A detailed perspective on the GPU IP architecture, clearly highlighting the area allocation for the graphics processing cluster, HBM3 interfaces, and chip-to-chip NVLINK connections
In-depth analysis of IP design with precise core dimension measurements
A concise overview of the progression of NVIDIA data center GPUs
Overview of NVIDIA GPU manufacturing supply chain
Analyze the economic factors of Hopper 200 GPU within the semiconductor industry and the data center GPU market

This full reverse costing study provides insights regarding the technology data, manufacturing cost, and selling price of the NVIDIA GPU H200 Hopper.

NVIDIA’s H200 GPU represents a significant advancement in high-performance computing, particularly in AI and HPC workloads, by enhancing memory bandwidth and capacity. While maintaining the same CoWoS packaging design as its predecessor, H100, the H200 incorporates HBM3 memory - delivering a major boost in throughput and power efficiency to support data-intensive tasks. Built on TSMC’s N4 process and leveraging the Hopper architecture, the H200 continues with high compute performance and optimized memory access. Notably, the IP architecture of the H200 is built entirely on NVIDIA-developed Tensor Cores, ensuring full-stack hardware innovation tailored for AI acceleration. Despite reusing the proven CoWoS-S package, the H200 achieves performance gains through memory subsystem improvements, demonstrating how NVIDIA continues to extract value from an established platform.

In this report, we perform an in-depth physical analysis using high-resolution imaging techniques, including optical microscopy (OM), scanning electron microscopy (SEM). Moreover, we place particular focus on detailed floorplan analysis, offering high-resolution die imaging and block-level identification that maps out the core compute array, HBM3 interface, L2 cache regions, and specialized acceleration units. This analysis provides critical insights into NVIDIA’s design priorities, IP block allocation, and die space efficiency - key to understanding how performance and scalability are achieved.

Our comprehensive cost structure breakdown highlights the economics behind the H200, covering bill of materials, wafer and packaging costs, and estimated margins. Given the premium pricing and high gross margins NVIDIA commands on this product, our analysis sheds light on the substantial profitability driven by strategic design. This level of detail supports competitive benchmarking, reverse engineering, and strategic sourcing decisions for stakeholders seeking insight into one of the industry’s most profitable AI accelerator.