We previously covered the difference between CPUs and GPUs and wanted to go further into our research, especially on GPUs. If you are a developer, researcher, or enthusiast who works with Artificial Intelligence (AI), Machine Learning (ML), or Deep Learning (DL), you know how important it is to have a powerful and reliable Graphics Processing Unit (GPU) to handle the complex computations required by these applications.

NVIDIA is one of the leading manufacturers of GPUs, and they have been constantly innovating and improving their products to meet the growing demands of the AI and ML community.

If you are new to NVIDIA’s GPU offerings, a range of information is available on our Cloud GPU products page.

Throughout this blog, we will compare four of their most advanced and high-performance GPUs: the A100, the L40s, the H100, and the H200 GH Superchips. We will look at each GPU's key specifications, features, and performance, see how they stack up against each other on various benchmarks and metrics, and provide some recommendations on the best GPU for machine learning, which one to choose, depending on your needs.

An overview of the NVIDIA GPU range

NVIDIA produces several top-tier GPUs suited for several workloads, such as gaming and advanced AI/ML workloads. This section provides a brief NVIDIA GPU comparison overview of four of their models: the A100, L40s, H100, and H200 GH Superchip.

An overview of the NVIDIA GPU range

NVIDIA A100 Tensor Core GPU: Introduced with the Ampere architecture, the A100 is a versatile GPU designed for a broad range of data center applications, balancing performance and flexibility.

NVIDIA L40S GPU: The L40s, part of the Ada Lovelace architecture, offers groundbreaking features and performance capabilities and is designed to take AI and ML to the next level.

NVIDIA H100 Tensor Core GPU: With the Hopper architecture, the H100 pushes the boundaries of GPU performance, targeting the most demanding AI and ML applications.

NVIDIA GH200 Grace Hopper Superchip: The GH200 promises to be NVIDIA's most advanced GPU yet, boasting significant improvements in core count, memory, and bandwidth.

Here is a summary table of the main characteristics of each GPU ↓

A100 L40s H100 GH200
Architecture Ampere Ada Lovelace Hopper Hopper
CUDA Cores 6,912 18,176 16,896
Tensor Cores 312 568 989
Memory Type HBM2e GDDR6 HBM2e HBM3
Memory Size 40GB or 80GB 48GB 80GB 141GB
Memory Bandwidth 2,039 GB/s 864 GB/s 3,350 GB/s 4,500 GB/s
Sparsity Support Yes Yes Yes Yes
MIG Capability Yes No Yes Yes
Power Consumption Up to 400W Up to 300W Up to 700W Up to 700W
Ideal for AI/LLM Inference & Training, 3D Graphics AI/LLM Inference & Training, 3D Graphics Large-scale AI Training, Conversational AI Generative AI, HPC Workloads
Release Year 2020 2023 2022 2024

Although we are concentrating on these four GPUs, newer models have recently been released in Nvidia's product range, such as the GeForce RTX 4070 Ti SUPER, NVIDIA Blackwell, and the upcoming RTX 50-Series GPUs.

Beyond the numbers, what do these differences mean for users? Let’s look at that:

CUDA Cores and Tensor Cores

The core counts on these NVIDIA GPUs are a pretty big deal when it comes to parallel processing power. CUDA Cores are the general-purpose processors that handle standard computing tasks, while Tensor Cores are specialized for accelerating machine learning and AI workloads. The more of these cores a GPU has, the more parallel computations it can perform simultaneously - crucial for demanding AI and ML applications. The higher CUDA and Tensor core counts of the NVIDIA H100, H200, and, to some extent, the L40 GPUs allow for faster parallel processing compared to the A100, with performance improvements scaling with workload parallelism. This means that the later models achieve superior performance in applications that can leverage increased parallelism, such as training large language models, running complex simulations, and processing massive datasets.

Memory Type and Size

The type, size, and speed of a GPU's memory determine what applications it can optimally support. Larger, faster options like HBM allow for bigger datasets and minimize bottlenecks.

  • The A100’s 40 GB - 80 GB of HBM2e memory is ample for many applications, but the H200’s 141 GB of HBM3 memory offers the largest and fastest memory, crucial for data-intensive applications like large-scale simulations or deep learning with massive datasets.
  • The L40s has GDDR6 memory with ECC, which may not be as fast as HBM memory but still provides significant storage for data.
  • The H100 matches the A100 in memory size and also uses HBM2e, providing high-speed data access that is beneficial for data-intensive tasks.

While the A100 memory is suitable for many tasks, the increased memory capacities of the H100 and especially H200 are better suited for data-intensive workloads that push the limits of what current GPUs can handle.

Memory Bandwidth

Transferring data efficiently between the memory and processor cores is crucial. Higher bandwidth means potential slowdowns are reduced, especially for data-intensive modeling.

  • The A100’s memory bandwidth of 2,039 GB/s supports efficient data transfer for various applications, but the H200’s highest memory of about 4,500 GB/s suggests it can handle the most data-intensive tasks with ease, reducing potential bottlenecks and improving overall performance.
  • The L40s with the least bandwidth of about 846 GB/s suggests it will likely reduce data transfer bottlenecks less than the other GPUs.

The high memory bandwidths of the H200 and H100 sets them above the other GPUS when there's a need to rapidly move massive amounts of data, especially for workloads where data transfer bottlenecks may occur, like with enormous Al models.

Sparsity Support

Sparsity support skips zero values in sparse AI models, doubling performance for certain workloads.

  • The A100 and L40s support sparsity, but they are not as efficient as newer Gracehopper Architecture like H100 and H200 in handling AI tasks involving sparse data.
  • The H100 and H200 are the most efficient in running AI models that involve sparse data, effectively doubling the performance for certain AI and ML tasks.

The Hopper architecture powering the H100 and H200 offers the most efficient sparsity handling, allowing these newer GPUs to excel at processing workloads involving AI models with many zero-valued connections, such as those commonly found in computer vision tasks.

MIG Capability

MIG capabilities provide workload flexibility when juggling multiple simultaneous tasks

  • The A100’s MIG capability allows for flexible workload management, but the H100 and H200’s MIG capabilities provide better resource allocation and versatility in multi-tenant environments or when running multiple different workloads simultaneously.
  • The L40s does not have MIG capability, which could limit its versatility compared to its counterparts.

Performance Benchmark

Let’s delve into the performance benchmarks of NVIDIA’s GPUs to provide a clearer understanding of how they perform in real-world scenarios.

NVIDIA A100: The A100 has been tested extensively and is known for its significant performance gains in AI and deep learning tasks. For instance, in language model training, the A100 is approximately 1.95x to 2.5x faster than the V100 when using FP16 Tensor Cores. It also scored 446 points on OctaneBench, claiming the title of the fastest GPU at the time of the benchmark.

NVIDIA L40s: L40s is reported to deliver A100-level performance for AI across a variety of training and inference workloads found within the MLPerf benchmark. However, with only 48GB of total VRAM, it underperforms when running large language models with significantly high parameters compared to the A100, which has 80GB of VRAM. It also shows promise with a 26% better performance in Geekbench - OpenCL compared to its predecessor.

NVIDIA H100: The H100 series, particularly the H100 NVL, shows a significant leap in computational power, especially in FP64 and FP32 metrics. This GPU is optimized for large language models (LLMs) and surpasses the A100 in specific areas, offering up to 30x better inference performance. It has also demonstrated improvements of up to 54% with software optimizations in MLPerf 3.0 benchmarks.

NVIDIA H200: Preliminary data suggests that the H200 will supercharge generative AI and high-performance computing (HPC) workloads with its larger and faster memory capabilities. It is expected to offer 1.9x faster inference for Llama2 70B and 1.6x faster for GPT-3 175B compared to the H100. Additionally, it is projected to deliver up to 110x faster performance in certain HPC applications.

Which GPU is right for you?

The best GPU for you will depend on your specific use case, preferences, and budget. Here are some general guidelines that may help you make a decision:

Use Cases Recommended GPUs
Reliable and versatile GPU for a wide range of workloads (scientific computing, AI/ML) A100
Graphics and animation applications, AI/ML with performance boost, realistic graphics and animations L40s
Cutting-edge, high-performing GPU for demanding AI/ML applications (natural language understanding, computer vision, recommender systems, generative modeling) H100
Future-ready, innovative GPU for the most cutting-edge and challenging AI/ML applications, exceeding H100 capabilities H200


In this blog, we've detailed a comparison of four of NVIDIA’s cutting-edge GPUs—the A100, L40s, H100, and H200—specifically designed for professional, enterprise, and data center applications. We explore these GPUs' architectures and technologies optimized for computational tasks, AI, and data processing. You'll find an in-depth look at their key specifications, features, and performance metrics, helping you understand how they compare across various benchmarks.

Check out how you can access each GPU of these GPUs with our NVIDIA cloud GPU range.

Whether you're deciding on the best GPU for your next project or just keeping up with NVIDIA’s innovations, we have the right solutions tailored to meet your diverse computational needs.

Discover which GPU is the ideal choice for your requirements and learn how to maximize your investment in high-performance computing. Upgrade today and power your projects with the best in technology.