What Differences Between NVIDIA A100, H100, L40S and H200?

The field of large-scale models has recently seen a flourishing landscape, largely supported by powerful computational capabilities. Taking ChatGPT as an example, it operates on a supercomputer built with Microsoft's investment, comprising tens of thousands of NVIDIA A100 GPUs distributed across more than 60 data centres. In this article, we will compare four of the most advanced GPUs: A100, H100, L40S, and H200. We will examine their main specifications, features, and performance, and compare them across different benchmarks and metrics.

The Core Architecture of GPU

Before exploring the differences between the A100, H100, L40S, and H200 GPUs, it is helpful to first gain a basic understanding of NVIDIA's core GPU architecture. This foundational knowledge will enable us to better comprehend the distinctions and advantages of each of these GPUs.

Volta Architecture (2017)

The Volta architecture introduced the Tensor Core, an exclusive hardware block designed specifically for deep learning and artificial intelligence tasks. The Tesla V100, built on the Volta framework, features 5120 CUDA cores, 320 texture units, and 128 ROP units. Volta also unveiled NVLink, a high-speed interconnect facilitating quicker data transfer between GPUs.

Turing Architecture (2018)

The Turing architecture marked a considerable shift from traditional GPU design, launching hybrid rendering and variable rate shading. The RTX 2080, built on Turing, comprised 2944 CUDA cores, 184 texture units, and 64 ROP units. Turing also brought AI-enhanced graphics to the fore, using deep learning to boost visual fidelity.

Ampere Architecture (2020)

The Ampere architecture is the latest evolution in Nvidia's GPU design. The A100, based on Ampere, includes 6912 CUDA cores, 432 texture units, and 192 ROP units. Ampere introduces Multi-Instance GPU (MIG), permitting multiple instances of a GPU to operate simultaneously, enhancing resource utilisation and reducing latency.

Hopper Architecture (2022)

The Hopper architecture is the ninth generation of NVIDIA GPUs. Compared to Ampere, Hopper supports fourth-generation Tensor Cores and utilises new streaming multiprocessors, each offering enhanced capabilities. Hopper introduces innovative improvements in computational power, deep learning acceleration, and graphics functions.

A100

The A100 is built on NVIDIA’s Ampere architecture and boasts several improvements over the previous Volta architecture, delivering 20 times the performance of its predecessor. In benchmarks, it excels in deep learning workloads such as image recognition, natural language processing, and speech recognition.

One of the key features of the Ampere architecture is its third-generation Tensor Cores, designed to accelerate AI workloads by performing matrix operations at higher speeds. Additionally, the A100 includes new hardware for improved data communication between GPU and CPU, namely NVIDIA’s Multi-Instance GPU (MIG) technology.

H100

The H100 is NVIDIA 9th generation data centre GPU, designed to achieve a significant performance leap over the previous generation NVIDIA A100 Tensor Core GPU for large-scale HPC. The H100 GPU seamlessly integrates with NVIDIA’s NVLink interconnect technology, enabling high-bandwidth communication between GPUs. This allows users to quickly and easily enhance computational performance, making it an ideal solution for large-scale machine learning and deep learning workloads.

The H100 utilises the NVIDIA Hopper GPU architecture, marking another major advancement in the accelerated computing performance of NVIDIA’s data centre platform. The H100 continues the main design focus of the A100, enhancing the scalability of AI and HPC workloads and significantly improving architectural efficiency. While the H100 and A100 have similar usage scenarios and performance characteristics, the H100 excels in handling more complex scientific simulations.

L40S

The L40S is designed to handle next-generation data centre workloads, including large language model (LLM) inference and training, 3D graphics rendering, and scientific simulations. Compared to its predecessors, such as the A100 and H100 GPUs, the L40S offers up to a 5x increase in inference performance and a 2x improvement in real-time ray tracing (RT) performance. It features 48GB of GDDR6 memory and supports ECC, which is essential for maintaining data integrity in high-performance computing environments.

Additionally, the L40S is equipped with over 18,000 CUDA cores, which are crucial for managing complex computational tasks. In summary, the L40S holds significant advantages in handling intricate and high-performance computational tasks. Its efficient inference capabilities and real-time ray tracing performance make it an indispensable asset in data centres.

H200

The H200 is the first GPU to feature 141 GB of HBM3e memory and 4.8 Tbps of bandwidth, nearly doubling the memory capacity and offering 1.4 times the bandwidth of the H100. In high-performance computing, the H200 can achieve up to 110 times the acceleration compared to CPUs, significantly speeding up computational results. When handling Llama2 70B inference tasks, the H200 inference speed is twice that of the H100 GPU. The H200 is poised to play a critical role in the Internet of Things (IoT) within edge computing and IoT applications.

Furthermore, the H200 is expected to deliver the highest GPU performance for large language model (LLM) training and inference, generative high-performance computing applications, including those involving the largest models with over 175 billion parameters. In summary, the H200 will provide unprecedented performance in the field of high-performance computing, particularly when processing large models and complex tasks.

Which GPU Is Best for You?

The ideal GPU for you depends on your specific use case, preferences, and budget. Here are some general guidelines that might help you make an informed decision.

FS H100 InfiniBand Solution

The current generation H100 and H200 GPUs are very similar in terms of multi-precision computational performance. Although the H200 offers performance improvements, considering cost-effectiveness, the H100 is likely to remain the preferred choice for users.

The FS H100 InfiniBand solution utilises the NVIDIA® H100 GPU, coupled with PicOS® software and the AmpCon™ management platform, customising network topology according to HPC architecture, including InfiniBand networks, management networks, and storage networks to meet various business needs.

This solution employs the NVIDIA® Quantum-2 MQM9790 InfiniBand switch, featuring 64 x 400Gb/s ports and supporting the latest high-speed interconnect technologies, including RDMA, adaptive routing, and NVIDIA's Scalable Hierarchical Aggregation and Reduction Protocol (SHARP). Combined with cost-effective InfiniBand OSFP 800G SR8 and OSFP 400G SR4 modules, the solution achieves speeds up to 400G/800G.

The chosen network card is the Mellanox ConnectX®-7, which delivers 400Gb/s InfiniBand ports, ultra-low latency, and 330 to 370 million messages per second. This architecture, paired with H100 GPU servers, enables the construction of high-performance, reliable, and scalable data centre computing networks.

Final Thought

In this article, we detail four cutting-edge NVIDIA GPUs (A100, L40S, H100, and H200), designed for professional, enterprise, and data centre applications. This comparison guide aims to help you choose the ideal NVIDIA GPU for deep learning, HPC, graphics, or virtualisation needs in your data centre or edge computing environment.

Whether you're selecting the best GPU for your next project or keeping up with NVIDIA's innovations, we offer customised solutions to meet your diverse computing needs. FS provides tailored solutions and precise configurations based on your budget, helping you effectively manage project costs.