A100 vs. L40s vs. H100 vs. H200 GH superchips: A comparison of NVIDIA’s next-gen GPUs

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.

Throughout this blog, we will compare four of their most advanced and high-performance GPUs: the A100, the L40s, and the H100. 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, and H100.

• NVIDIA A100 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.

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

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, and, to some extent, the L40s 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.
• The L40s have 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 is 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.
• 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 H100 sets it 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 in handling AI tasks involving sparse data.
• The H100 is 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 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'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.

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:

Deploying your first GPU instance on Civo

Getting started with Civo GPU compute is straightforward. The process from account creation to a running GPU instance takes just a couple of minutes:

1. Create a Civo account at civo.com: New accounts receive $250 in free credit to explore the platform
2. Navigate to GPU instances in the Civo dashboard and select your preferred GPU
3. Choose your configuration: Select region, instance size, and whether you need on-demand or committed pricing
4. Select your base image: Civo provides pre-configured ML images with PyTorch, TensorFlow, CUDA, and cuDNN pre-installed, or bring your own container image
5. Connect and run : SSH directly into your instance or use one-click Jupyter access to begin your workload immediately

For teams looking to automate GPU provisioning as part of a CI/CD pipeline or MLOps workflow, Civo's API and Terraform provider support programmatic instance management - provision, configure, and tear down GPU instances without touching the dashboard.

What makes Civo's GPU cloud different

GPU access is only part of the equation. The infrastructure surrounding the GPU (orchestration, networking, developer experience, and support) determines how quickly your team goes from provisioning to production.

Kubernetes-native from the ground up

Civo's infrastructure is native to Kubernetes, ensuring rapid deployment, dynamic auto-scaling, and a modern networking architecture that grows with your workloads. Teams benefit from bare metal performance through Kubernetes by eliminating hypervisors from the stack and deploying containerized workloads directly.

This matters for ML teams because Kubernetes is the operational layer most production AI pipelines are already built around. There is no translation layer, no proprietary orchestration system to learn, and no lock-in to a custom scheduling interface.

Transparent pricing with no egress fees

One of the most significant hidden costs in GPU cloud is data egress - moving large model artifacts, training datasets, and inference outputs in and out of the provider's network. Civo eliminates egress fees entirely, which means the cost you see when you provision a GPU instance is the cost you pay.

For teams running iterative training workflows or serving models that require frequent checkpoint uploads, this is not a minor consideration - it is a meaningful component of total cost of ownership.

On-demand availability without quota friction

In the GPU market right now, "on-demand" often means something closer to "available eventually, probably, if you've planned ahead."

Quota approvals, regional availability constraints, and waitlists are routine frustrations with hyperscaler GPU access. Civo's on-demand model means genuine self-serve access. You can provision an A100 or H100 instance through the Civo dashboard or API without submitting a support ticket or waiting for quota approval.

Pre-configured ML frameworks

Civo GPU instances come with pre-installed popular ML frameworks, including PyTorch, TensorFlow, CUDA, and cuDNN, all readily available - with one-click Jupyter access and the ability to connect to GPU instances directly from a browser.

For teams that want to move from provisioning to running their first training job in minutes rather than hours, this removes a significant amount of setup friction.

Why GPU choice matters for ML teams in 2026

Not all GPU deployments are equal, and in a market where the H100 is available from 41 cloud providers with significant pricing and availability variance across regions and billing models, the decision of where and how you access GPU compute has a direct impact on project timelines, costs, and outcomes.

On-demand GPU compute has become the defining infrastructure question for AI teams in 2026, and yet the gap between what providers advertise and what they actually deliver has never been wider.

NVIDIA's A100 and H100 are the chips everyone wants. The question worth asking is whether you can actually get them when you need them, at a price that does not require a CFO sign-off every time you kick off a training run.

At Civo, we cut through that complexity with transparent, on-demand access to both A100 and H100 GPUs - backed by Kubernetes-native infrastructure, no egress fees, and pricing designed to make serious ML workloads financially predictable.

Summary

In this blog, we've detailed a comparison of four of NVIDIA’s cutting-edge GPUs—the A100, L40s, and H100—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.

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