NVIDIA H100 GPU : Specifications, Architecture, Working, Differences & Its Applications

Hopper or micro-architecture is a GPU (Graphics Processing Unit) launched by Nvidia in March 2022. This microarchitecture is primarily designed for data centers and is also used in conjunction with the Lovelace microarchitecture. It is the most recent generation of the Nvidia Tesla, but currently, it is known as Nvidia Data Centre GPUs. It was named for an American computer scientist, mathematician, and also a US Navy rear admiral, “Grace Brewster Hopper”. These GPUs are the latest production of high-performance graphics processors, which are built for high-performance computing and AI. This article elaborates on the NVIDIA H100 GPU, its working, and its applications.

What is the NVIDIA H100 GPU?

The NVIDIA H100 is the most powerful graphics processing unit chip, designed especially for AI (artificial intelligence) applications. The H100 GPU includes 80 billion transistors, which are six times above than the previous generation’s A100 GPU. It processes large amounts of data much faster than other graphics processing units. The H100 GPUs have more demand because of their great performance and capability of accelerating AI applications.

Usually, AI applications need more processing power to instruct and run. So the powerful computing capabilities of the H100 chip make it perfect for these applications. In addition, it can also be used for developing many medical diagnosis systems, self-driving cars & AI-powered applications.

How does NVIDIA H100 Work?

The high-performance NVIDIA H100 GPU works by using the Hopper architecture & fourth-generation Tensor Cores in HPC (high-performance computing) and AI workloads. It performs well at different tasks like complex scientific simulations and training large language models.

The H100 is built on the Hopper architecture of NVIDIA to introduce major performance developments over earlier generations. This GPU supports a variety of precision types like FP16, FP8, FP64, and FP32 by allowing flexibility for various workloads.

The H100 can create a powerful system mainly for exascale workloads by combining up to 256 GPU clusters. NVLink switch system helps to communicate between GPUs within these clusters to ensure very efficient operation even through great numbers of GPUs. The features of H100 include enhanced MIG (Multi-Instance GPU) technology, high-bandwidth NVLink connections for multi-GPU setups, and a Transformer Engine for accelerated AI.

NVIDIA H100 Specifications

The specifications of NVIDIA H100 include the following.

• The NVIDIA H100 chip is a high-performance data center GPU. It is based on Hopper architecture and is designed mainly for inference, AI training, and HPC (high-performance computing) tasks.
• GPU Architecture is NVIDIA Hopper.
• It uses 80 billion transistors
• Process size is 4N (TSMC).
• Memory is 80 GB HBM2e (PCIe version)/80 GB HBM3 (SXM version)/
• It has 50 MB L2 cache and 2 TB/s memory bandwidth.
• Its BW is nearly 2x of A100 by HBM3.
• Fourth-generation Tensor Cores provide up to 9x faster AI training as compared to A100 through Transformer Engine & FP8.
• FP8 support allows up to 4x the compute FP16 throughput on A100.
• Its transformer engine optimizes transformer models, which provides up to 9x high performance on 30x faster inference & AI training.
• The second-generation multi-instance GPU provides more computing capability & memory bandwidth for each instance as compared to the A100.
• Interconnect – NVLink & PCIe Gen 5.
• Power is up to 700 watts.
• Compute performance (FP64 is 60 teraflops and FP8 is over 1000 teraflops).
• The software is NVIDIA AI Enterprise.

Difference between NVIDIA A100 and NVIDIA H100 GPUs

The most powerful GPUs, like NVIDIA A100 & NVIDIA H100, are designed mainly to handle the most challenging workloads within AI, high-performance computing, and machine learning. These GPUs provide the best performance for major AI training to managing complex datasets, but they differ in features. The main difference between NVIDIA A100 and NVIDIA H100 GPUs lies in their performance and memory configurations. So the difference between NVIDIA A100 and NVIDIA H100 GPUs includes the following.

NVIDIA H100 Architecture

The NVIDIA H100 is a high-performance GPU based on the Hopper architecture, specifically designed for accelerating AI workloads, mainly LLMs (large language models) & other generative AI systems. It provides improvements in NVLink, a new Transformer Engine, Tensor Cores & the Hopper micro-architecture, extensively improving performance as compared to the A100.

TSMC 4N Process

The NVIDIA H100 FPU is designed with a 4-nanometer custom process from TSMC, which allows for a higher density of 80 billion transistors & better performance for each watt as compared to the earlier generation. The H100 GPU with this fabrication process increases GPU core frequency, and progress performance, and includes more TPCs, GPCs & SMs than the previous generation, which depended on the TSMC 7nm N7 procedure.

Streaming Multiprocessors or SMs

The NVIDIA H100 includes up to 144 SMs, and each SMs has significant performance enhancements as compared to A100. These are almost similar to the CPU cores; thus, they both can execute computations & store available states for computation within registers using related caches. GPU streaming multiprocessors are weak and simple processors as compared to CPU cores. SMs’ execution is pipelined in an instruction; however, there is no approximate instruction pointer prediction or execution. However, GPU SMs can perform more threads in parallel.

The streaming multiprocessors of this GPU support a large number of concurrent threads. So a single streaming microprocessor on an H100 GPU can implement up to 2048 threads, which are divided across 64 groups of threads for 32 threads each.

Tensor Cores

This GPU includes fourth-generation Tensor Cores, which deliver significant performance gains for AI workloads. It supports FP8 data types & a new Transformer Engine that speeds up large language models. In addition, these fourth-generation Tensor Cores speed up matrix operations, mainly for deep learning workloads. So, these cores are significant components in an H100 GPU, which allows significant leaps within AI training & inference as compared to earlier generations.

Transformer Engine

The Transformer Engine in NVIDIA’s H100 GPU is a hardware & software feature designed to speed up the inference and training of Transformer models, which are essential for large language models. This can be achieved by managing mixed precision calculations intelligently with 8-bit floating point (FP8) and 16-bit floating point (FP16). This transformer engine optimizes the GPU for neural networks by providing 9x faster AI training & 30x faster AI inference speedups on huge language models.

NVLink

This GPU uses the fourth-generation NVLink 4.0 by allowing faster GPU-to-GPU communication and low latency across several nodes. It is essential for HPC and large-scale AI deployments. This NVLink provides a significant increase in bandwidth as compared to earlier versions. The H100 GPU delivers a total of 900 GB/s NVLink bandwidth for each GPU. So this can be attained using 18 NVLink 4.0 links, where each provides 50 GB/s bidirectional bandwidth.

Multi-Instance GPU/MIG

The H100 GPU uses MIG technology to provide safe & isolated GPU instances for a variety of workloads. So MIG GPU expands the value and performance of NVIDIA Blackwell as well as Hopper generation GPUs. In addition, it can also divide the GPU into seven instances where every isolated GPU has its cache, compute cores, and high-bandwidth memory. This provides the ability to administrators to support each workload with assured QoS (quality of service). It extends the accelerated computing resources to each user.

Memory System

The NVIDIA H100 GPU has a memory system and is built around HBM3 technology. This GPU has a larger L2 cache & 3 TB/s increased memory bandwidth, which improves performance for mainly memory-intensive tasks.

It provides high capacity and bandwidth for demanding AI & high-performance computing workloads. It boasts up to 80GB of HBM3 memory, including 3.35 TB/s BW. So this is a major upgrade from the earlier generation, which provides double the speed. Further, the H100 can also include a large L2 cache & increased L1 cache for each streaming multiprocessor to improve memory performance.

DPX Instructions

The NVIDIA H100 GPU provides new DPX instructions mainly for dynamic programming algorithms to speed up computations within a variety of algorithms. In addition, it can also provide improvements in significant performance over earlier generations. So these instructions are based on hardware that improves the efficiency directly for DP algorithms, which are utilized in a variety of fields like logistics optimization, graph analytics & disease diagnosis.

CUDA Hierarchy

A CUDA hierarchy is a thread block cluster in a GPU that improves efficiency and locality, mainly for a lot of algorithms. The hierarchy in CUDA is a structured thread organization with associated memory, designed for parallel execution on NVIDIA GPUs. So this hierarchy includes three main levels: threads, a grid of blocks, and blocks. Every level includes the right of entry to particular memory spaces, which creates a memory hierarchy that balances the thread hierarchy.

HBM3 Memory

The H100 uses HBM3 memory, which offers higher bandwidth and capacity compared to the previous generation for demanding HPC & AI workloads. The SXM version especially boasts HBM3 memory – 80GB with 3.35 TB/s BW. So this is almost double the bandwidth of the earlier generation, which allows the GPU to feed its Tensor Cores & SMs efficiently. The H100 NVL version features 94GB of HBM3 & 3,938 GB/s of BW.

NVIDIA H100 GPU Software Support

This GPU supports powerful software tools that allow enterprises and developers to design and speed up applications from Artificial Intelligence to HPC. So this software mainly includes updates for workloads like recommender systems, hyperscale inference, speech, etc.

In addition, NVIDIA can also release more than 60 updates to its library collection of CUDA-X™ technologies and tools to speed up work in 6G research, quantum computing, cybersecurity, drug discovery, and genomics.

The NVIDIA H100 GPU supports software like the DGX OS, NVIDIA AI Enterprise suite, and a variety of drivers & libraries that allow its high performance within AI, data analytics, and HPC.

NVIDIA AI Enterprise

It is a complete software suite, mainly designed to simplify AI development & operation on NVIDIA H100 GPUs. This software includes frameworks, tools, and libraries optimized for different AI tasks. In addition, it can also provide support & training resources for operators. This GPU’s performance can be improved significantly whenever connected with NVIDIA AI Enterprise to tackle significant AI workloads capably.

DGX OS

NVIDIA DGX systems with H100 GPUs use the DGX operating system, a customized Ubuntu Server version. This operating system includes a range of software packages like NVIDIA System Management/NVSM, Data Center GPU Management/ DCGM, NVIDIA GPU driver, Docker engine & NVIDIA container toolkit, networking software, and cache files.

Other Softwares

A microservices framework on H100 GPU simplifies scaling and deploying AI models. It allows operators to drag Docker images, run models through an API, and communicate with them through

Python Scripts

The CUDA toolkit is basic software, used to program the NVIDIA H100 GPU. A GPU-accelerated library used for deep neural networks for optimizing AI workloads. A GPU-accelerated library used for linear algebra operations is essential for several scientific & AI computations.

Operating Systems

The NVIDIA H100 PCIe GPU supports different operating systems, which include Linux distributions like CentOS, RHEL, Ubuntu, Windows Server, etc. In addition, it can also be used to develop & test on Windows 11.

Cloud Services

Cloud services like Paperspace and DigitalOcean provide H100 instances using pre-installed software & NVIDIA drivers, which simplify the procedure starting with the GPU. So these platforms give various options to create & manage GPU-powered virtual machines for machine learning and AI tasks. Basically, the NVIDIA H100’s abilities can be amplified through a strong software ecosystem to make it a powerful platform for a wide range of tasks.

Advantages

The advantages of NVIDIA H100 include the following.

• The NVIDIA H100 delivers outstanding performance, safety, and scalability for every workload.
• Its Tensor Core technology delivers superior performance, which allows for faster training & AI model inference.
• This GPU is perfect for image, video, and text generation through AI models.
• It provides superior AI performance by SXM architecture, high-speed networking, NVLink & flexible storage options.
• The H100 provides great FP16 and FP8 Tensor Core performance in data-intensive and complex workloads like the LLMs.
• It supports fourth-generation NVLink & NVSwitch.
• It can be divided into up to seven separate GPU instances by allowing the simultaneous multiple workloads execution with dedicated resources.
• This GPU has an integrated safety function that protects the whole workload.
• It provides a powerful memory solution mainly for data-intensive workloads.

Disadvantages

The disadvantages of NVIDIA H100 include the following.

• These are expensive.
• Its availability is limited due to higher demand.
• The NVIDIA H100 GPUs are more power-efficient due to their high power consumption.
• It needs a server with strong power, connectivity, and cooling capabilities.

Applications

The applications of NVIDIA H100 include the following.

• The NVIDIA H100 GPU is used for AI and high-performance computing applications, mainly for training & inference of complex AI models and large language models.
• In addition, it can also be used in smart cities, a variety of scientific computing fields, data centers, etc.
• It is essential for training & deploying LLMs in translation, question-answering, and natural language processing.
• This GPU can also accelerate the deployment and development of generative AI models, allowing the new content like text, images, and more.
• It is powerful in handling complex computer vision tasks.
• It speeds up training & inference within data centers, optimizing performance for a variety of AI workloads.
• AI-driven financial models profit from the performance of GPUs, allowing faster & more precise risk assessments & fraud detection.
• It provides the required computational power for complex simulations within scientific fields like biology, physics, climate modeling, etc.
• Researchers can influence the H100 to undertake large datasets and difficult scientific challenges to drive innovation across a variety of disciplines.
• The H100 GPU is well known for computing, thus used to create high-quality graphics & simulations.
• Other applications of NVIDIA H100 involve IoT, smart cities, autonomous vehicles, virtualization, confidential computing, etc.
• It protects AI models and sensitive data, mainly in regulated industries.

Thus, this is an overview of the NVIDIA H100 GPU, which delivers equal to 9x faster AI training & 30x quicker AI inference as compared to earlier generations. So it is ideal for generative AI and large language models. Its Transformer Engine and fourth-generation Tensor Cores are particularly optimized to handle complex AI workloads. Here is a question for you: What is NVIDIA GB200?