A brief comparison of NVIDIA A100 H100 L40S and H200

In the data center market of 2024, NVIDIA GPUs remain in high demand with persistent supply constraints. Since launching the A100 in 2020, NVIDIA has iterated its product line with the H100 in 2022, followed by the L40S in 2023 and the H200 in 2024.

The following table summarizes key parameters these products.

A100

The A100, introduced in 2020, marked the first utilization of the Ampere architecture for GPUs, bringing significant performance improvements.

Before the release of the H100, the A100 outshined all other GPUs. Its performance gains were attributed to enhanced Tensor cores, increased CUDA core count, improved memory, and the fastest 2 Tbps memory bandwidth.

The A100 supports Multi-Instance GPU (MIG) functionality, which allows a single A100 GPU to be partitioned into multiple independent smaller GPUs, greatly improving resource allocation efficiency in cloud and data center environments.

Although surpassed by newer models, the A100 remains an excellent choice for training complex neural networks, deep learning, and AI workloads, thanks to its Tensor cores and high throughput, which deliver outstanding performance in these domains.

The A100 excels in AI inference tasks and demonstrates advantages in various applications such as speech recognition, image classification, recommendation systems, data analysis, big data processing, scientific computing, as well as high-performance computing scenarios like genomic sequencing and drug discovery.

H100

The H100 is capable of handling the most challenging AI workloads and large-scale data processing tasks.

The H100 features upgraded Tensor cores, resulting in a significant improvement in the speed of AI training and inference. It supports computations in double precision (FP64), single precision (FP32), half precision (FP16), and integer (INT8) formats.

Compared to the A100, the H100 offers a six-fold increase in FP8 computation speed, achieving up to 4 petaflops. It also boasts a 50% increase in memory capacity, utilizing HBM3 high-bandwidth memory with a bandwidth of up to 3 Tbps. The external connectivity speed is nearly 5 Tbps. Additionally, the new Transformer engine enhances model transformer training speed by up to six times.

While the H100 and A100 share similarities in usage scenarios and performance characteristics, the H100 outperforms the A100 in handling large-scale AI models and more complex scientific simulations. The H100 is a superior choice for real-time responsive AI applications such as advanced conversational AI and real-time translation.

In summary, the H100 offers significant performance improvements compared to the A100 in terms of AI training and inference speed, memory capacity and bandwidth, as well as processing large and complex AI models. It is suitable for AI and scientific simulation tasks that demand higher performance.

L40S

The L40S is designed to handle next-generation data center workloads, including generative AI, large-scale language model (LLM) inference and training, 3D graphics rendering, scientific simulations, and more.

Compared to previous-generation GPUs like the A100 and H100, the L40S offers up to a 5x improvement in inference performance and a 2x improvement in real-time ray tracing (RT) performance.

In terms of memory, it is equipped with 48GB of GDDR6 memory and includes support for ECC, which is crucial for maintaining data integrity in high-performance computing environments.

The L40S features over 18,000 CUDA cores, which are parallel processors essential for handling complex computational tasks.

While the H100 focuses more on decoding, the L40S places greater emphasis on visualization and encoding capabilities. Despite being slower than the H100, the L40S is relatively more accessible in terms of availability and price in the market.

In conclusion, the L40S offers significant advantages in handling complex and high-performance computing tasks, particularly in the fields of generative AI and large-scale language model training. Its efficient inference performance and real-time ray tracing capabilities make it a compelling option for data centers.

H200

The H200 is the latest addition to the NVIDIA GPU series and started shipping in the second quarter of 2024.

The H200 is the first GPU to offer 141 GB of HBM3e memory and a bandwidth of 4.8 Tbps, which is nearly twice the memory capacity and 1.4 times the bandwidth of the H100.

In terms of high-performance computing, the H200 achieves up to 110 times acceleration compared to CPUs, resulting in faster results.

When handling Llama2 70B inference tasks, the H200 demonstrates twice the inference speed of the H100 GPU.

The H200 plays a key role in edge computing and Internet of Things (IoT) applications, specifically in the domain of Artificial Intelligence of Things (AIoT).

Expect the H200 to deliver the highest GPU performance in applications such as training and inference of the largest models (exceeding 17.5 billion parameters), generative AI, and high-performance computing.

In summary, the H200 provides unprecedented performance in the fields of AI and high-performance computing, particularly in handling large-scale models and complex tasks. Its high memory capacity and bandwidth, along with exceptional inference speed, make it an ideal choice for processing cutting-edge AI workloads.

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