Unveiling the Game-Changer: Nvidia Hopper GPU

Table of Contents:

1. Introduction
2. Overview of the NVIDIA Ampere GPU
3. Features of the NVIDIA Hopper GPU
   • 3.1 Fabrication Process and Die Size
   • 3.2 Memory and Packaging
   • 3.3 Interconnect Technology
   • 3.4 Performance Improvements
   • 3.5 Tensor Cores and Lower Precision Compute
   • 3.6 Configurable Floating Point Formats
   • 3.7 New Instruction Set - GPX
   • 3.8 Power Consumption and Cooling
4. Benchmarking and Comparison with AI Accelerators
   • 4.1 Cerebras Wafer-Scale Engine
   • 4.2 Graphcore IPU
   • 4.3 Google TPU v4
   • 4.4 Tesla's Dojo AI Accelerator
5. NVIDIA EOS Supercomputer
6. Grace Hopper Superchip
7. Conclusion

Article:

Introduction

NVIDIA has recently announced the release of its new GPU, the Hopper GPU, which is set to replace the popular Ampere GPU. The NVIDIA Ampere GPU has been hailed as the company's most successful GPU architecture to date, and now, the Hopper GPU is here to take its place. Designed for cloud AI compute and high-performance computing (HPC) applications in data centers, the Hopper GPU is set to revolutionize the industry with its impressive features and performance improvements.

Overview of the NVIDIA Ampere GPU

Before diving into the details of the Hopper GPU, it's important to understand the significance of its predecessor, the NVIDIA Ampere GPU. The Ampere GPU was a game-changer in the AI computing world, providing remarkable performance and groundbreaking features. Its architecture introduced tensor cores, which greatly accelerated matrix operations for machine learning tasks. With the Ampere GPU, NVIDIA set a new standard for AI computing, and all future AI accelerators were benchmarked against it.

Features of the NVIDIA Hopper GPU

The Hopper GPU boasts several significant features that make it a worthy successor to the Ampere GPU. Let's explore these features in Detail:

3.1 Fabrication Process and Die Size

The Hopper GPU is fabricated by TSMC using a custom 4nm process node. This process node represents a remarkable leap from the 7nm process used in the Ampere GPU. The use of the 4nm process node brings several advantages, including better power efficiency, increased speed, and higher transistor density. Despite the jump in process nodes, the die size of the Hopper GPU remains similar to that of the Ampere GPU, at around 800 millimeters square. Additionally, the Hopper GPU features an impressive 80 billion transistors, marking a 1.6 times increase compared to the Ampere GPU.

3.2 Memory and Packaging

In terms of memory and packaging, the Hopper GPU utilizes advanced technologies to enhance its performance. The GPU features 80 gigabytes of high-bandwidth HBM3 memory. Unlike the traditional 3D stacking method of packaging, the Hopper GPU employs a 2.5D packaging approach, which combines multiple dies in one Package. This packaging method, known as chiplet packaging, offers improved memory bandwidth and efficiency.

3.3 Interconnect Technology

To facilitate efficient communication between GPUs in a cloud environment, the Hopper GPU utilizes the NvLink4 interconnect technology. This technology provides 1.5 times higher bandwidth compared to the previous NvLink3 used in the Ampere GPU. The NvLink interconnect allows GPUs to exchange data directly, bypassing the need to go through the PCI Express interface. With this faster interconnect, up to 256 GPUs can be connected, enabling high-speed data exchange.

3.4 Performance Improvements

One of the most significant aspects of the Hopper GPU is its unparalleled performance improvements. The GPU introduces fourth-generation tensor cores, which are up to six times faster in low precision compute compared to the Ampere GPU cores. By focusing on lower precision compute, specifically the FP8 format, the Hopper GPU achieves remarkable performance improvements while saving resources such as memory and power. In FP8 format, the Hopper GPU is capable of 2,000 teraflops of compute, three times higher than the Ampere GPU. It also delivers one exaflop of AI compute in FP16 format, delivering three times the performance of the Ampere GPU.

3.5 Tensor Cores and Lower Precision Compute

The Hopper GPU's performance improvements can be attributed to the new fourth-generation tensor cores. These specialized cores are designed to perform matrix multiply and accumulate operations, commonly used in neural network computations. By focusing on lower precision compute, such as FP8 and FP16 formats, the Hopper GPU achieves higher throughput and efficiency. Storing values in FP8 format saves memory and reduces the amount of data transferred to and from memory, resulting in power savings.

3.6 Configurable Floating Point Formats

To further optimize performance and flexibility, the Hopper GPU introduces a new instruction set called GPX. This instruction set simplifies and accelerates the execution of complex machine learning algorithms. The Hopper GPU utilizes both FP8 and FP16 formats, with configurability for different parameters. This configurability allows for the allocation of different amounts of data for the exponent and fraction, optimizing performance Based on specific requirements.

3.7 Power Consumption and Cooling

It's important to note that the Hopper GPU comes with a substantial power consumption of 700 watts. This high power requirement necessitates both air and liquid cooling to adequately dissipate heat. Despite the jump in process nodes and advancements in technology, the Hopper GPU's power consumption remains significantly higher than its predecessor. As such, cooling solutions are crucial to ensure optimal performance and prevent thermal throttling.

Benchmarking and Comparison with AI Accelerators

The Hopper GPU's arrival has sparked a Wave of excitement within the AI accelerator market. Startups and established players alike will now benchmark their AI accelerators against the new NVIDIA GPU. Let's take a look at some key players in the AI accelerator space and how they compare to the Hopper GPU:

4.1 Cerebras Wafer-Scale Engine

The Cerebras Wafer-Scale Engine is a cutting-edge AI accelerator that utilizes an entire wafer for computation. It offers massive parallelism and reduces the communication bottleneck commonly faced by traditional GPUs. While the Hopper GPU boasts impressive performance improvements, the Cerebras Wafer-Scale Engine provides unique advantages through its wafer-scale architecture. Each of these accelerators will have its own strengths and applications, and it will be interesting to see how they compete in the market.

4.2 Graphcore IPU

Graphcore's Intelligence Processing Unit (IPU) is another notable AI accelerator that focuses on processing large-scale graph computations. The IPU's architecture is designed to handle complex machine learning algorithms efficiently. Like the Hopper GPU, the IPU utilizes lower precision compute, but each accelerator has its own unique capabilities and optimizations. Depending on specific use cases, the Hopper GPU or the Graphcore IPU may offer superior performance and efficiency.

4.3 Google TPU v4

Google's Tensor Processing Unit (TPU) v4 is a powerful AI accelerator designed specifically for machine learning workloads. The TPU v4 delivers impressive performance and energy-efficiency, showcasing Google's expertise in AI hardware development. With the Hopper GPU's arrival, it will be interesting to see how it stacks up against the TPU v4 in terms of performance, versatility, and market adoption.

4.4 Tesla's Dojo AI Accelerator

Tesla's Dojo AI accelerator, currently under development, aims to surpass the performance of NVIDIA's 800 GPU racks. While limited information is available about the Dojo accelerator, it is expected to deliver impressive performance improvements in AI compute. The Hopper GPU's success will be benchmarked against the Dojo accelerator, highlighting the ongoing competition in the AI accelerator market.

NVIDIA EOS Supercomputer

In addition to the Hopper GPU, NVIDIA has also announced the NVIDIA EOS supercomputer. Comprising 18 superpods with a total of 576 DJX Hopper systems, the EOS supercomputer aims to deliver impressive AI compute capabilities. With a staggering 18 exaflops of FP8 compute and 9 exaflops of FP16 compute, the EOS supercomputer is set to outperform previous NVIDIA supercomputers like Selene. This massive computing power will accelerate scientific research and push the boundaries of AI advancements.

Grace Hopper Superchip

NVIDIA further announced the development of the Grace Hopper superchip, an ARM-based server chip featuring 144 cores. This chip, when coupled with the Hopper GPU, offers remarkable performance improvements. The Grace Hopper superchip is expected to be 50% faster than AMD's EPYC CPU, marking a significant milestone in the server chip market. With low-power LP DDR5 memory and a terabyte per Second memory bandwidth, the Grace Hopper superchip aims to disrupt the traditional server CPU market and provide exceptional performance for data center workloads.

Conclusion

The NVIDIA Hopper GPU represents a significant advancement in AI computing and sets a new benchmark for AI accelerators. With its impressive performance improvements, enhanced interconnect technology, and focus on lower precision compute, the Hopper GPU showcases NVIDIA's commitment to pushing the boundaries of AI capabilities. As the industry evolves, it will be fascinating to see how other AI accelerators compare to the Hopper GPU and how they Shape the future of AI computing.

Highlights:

• NVIDIA introduces the Hopper GPU, replacing the successful Ampere GPU.
• The Hopper GPU is designed for cloud AI compute and HPC applications.
• Fabricated using a custom 4nm process node, the Hopper GPU offers improved power efficiency and transistor density.
• The GPU features 80 billion transistors and utilizes HBM3 memory with increased bandwidth.
• The NvLink4 interconnect technology enables direct and fast data exchange between GPUs.
• The Hopper GPU introduces fourth-generation tensor cores, enhancing performance in low precision compute.
• Configurable floating point formats provide flexibility and optimization for different machine learning algorithms.
• The Hopper GPU's power consumption is 700 watts, requiring efficient cooling solutions.
• Benchmarking the Hopper GPU against other AI accelerators like Cerebras, Graphcore, Google TPU, and Tesla Dojo will be fascinating.
• NVIDIA EOS supercomputer and Grace Hopper superchip further demonstrate NVIDIA's commitment to pushing the boundaries of AI compute.

FAQ

Q: What is the NVIDIA Hopper GPU? A: The NVIDIA Hopper GPU is the latest GPU architecture announced by NVIDIA. It is designed for cloud AI compute and high-performance computing applications in data centers.

Q: How does the Hopper GPU compare to the Ampere GPU? A: The Hopper GPU is a significant improvement over the Ampere GPU in terms of performance, efficiency, and features. It introduces fourth-generation tensor cores, lower precision compute, and advanced interconnect technology.

Q: What is the power consumption of the Hopper GPU? A: The Hopper GPU has a power consumption of 700 watts, making efficient cooling solutions essential for optimal performance.

Q: How does the Hopper GPU compare to other AI accelerators? A: The Hopper GPU will be benchmarked against other AI accelerators like Cerebras, Graphcore, Google TPU, and Tesla Dojo. Each accelerator has its own unique features and optimizations, catering to different use cases.

Q: When will the Hopper GPU be available? A: The Hopper GPU is expected to be available in the first half of 2023.

Q: What is the NVIDIA EOS supercomputer? A: The NVIDIA EOS supercomputer is a massive computing system comprising 18 superpods and 576 DJX Hopper systems. It aims to deliver exceptional AI compute capabilities.

Q: What is the Grace Hopper superchip? A: The Grace Hopper superchip is an ARM-based server chip developed by NVIDIA. It features 144 cores and is expected to be 50% faster than AMD's EPYC CPU.