13 minute read

The NVIDIA A100 vs H100, How Do They Compare?

Emmanuel Ohiri

Emmanuel Ohiri

Graphics Processing Units (GPU) accelerators have become a critical piece of technology. With advances in artificial intelligence (AI) and the exponential growth in data generation, high-performance computing (HPC), and advanced graphics workloads, the need for powerful computational resources has never been greater. With their parallel processing capabilities, GPU accelerators have emerged as a vital tool to handle these data-intensive tasks efficiently, leading to faster insights and real-time decision-making.

NVIDIA, a leading name in the tech landscape, is at the forefront of this GPU revolution. Their A100 and H100 GPUs are game-changers designed to handle demanding computing tasks efficiently. The NVIDIA A100, powered by the Ampere architecture, has set new standards for accelerating AI, HPC, and graphics workloads. It offers unprecedented performance and flexibility, making it a go-to choice for data centers and research institutions.

On the other hand, the NVIDIA H100, the latest in the line-up, takes performance to a whole new level. It is designed to deliver unmatched acceleration for AI, HPC, and graphics, allowing users to tackle some of the most challenging computational problems. With these GPUs, NVIDIA continues to shape the future of technology, pushing the boundaries of what's possible in digital computation. This article compares the NVIDIA A100 and H100 GPUs, highlighting their architecture, performance benchmarks, AI capabilities, and power efficiency.

Comparing the A100 and H100 architecture

The A100 and H100 GPUs have been designed specifically for AI and HPC workloads, driven by distinct architectural philosophies. Here is how they compare against each other:

NVIDIA A100's Ampere Architecture

The NVIDIA A100 Tensor Core GPU, powered by the revolutionary NVIDIA Ampere architecture, represents a significant advancement in GPU technology, particularly for high-performance computing (HPC), artificial intelligence (AI), and data analytics workloads.

This architecture builds upon the capabilities of the previous Tesla V100 GPU, adding numerous new features and significantly faster performance.

Key features of the A100 and its Ampere architecture include:

Third-Generation Tensor Cores:

These cores substantially boost throughput over the V100 and offer comprehensive support for deep learning and HPC data types. They offer new Sparsity features that double throughput, TensorFloat-32 operations for accelerated FP32 data processing, and new Bfloat16 mixed-precision operations.

Advanced Fabrication Process:

The Ampere architecture-based GA100 GPU, which powers the A100, is fabricated on the TSMC 7nm N7 manufacturing process. It includes 54.2 billion transistors and offers increased performance and capabilities.

Enhanced Memory and Cache:

The A100 features a large L1 cache and shared memory unit, providing 1.5x the aggregate capacity per streaming multiprocessor (SM) compared to the V100. It also includes 40 GB of high-speed HBM2 memory and a 40 MB Level 2 cache, substantially larger than its predecessor, ensuring high computational throughput.

Multi-Instance GPU (MIG):

This feature allows the A100 to be partitioned into up to seven separate GPU instances for CUDA applications, providing multiple users with dedicated GPU resources. This enhances GPU utilization and provides quality of service and isolation between different clients, such as virtual machines, containers, and processes.

Third-Generation NVIDIA NVLink:

This interconnect technology enhances multi-GPU scalability, performance, and reliability. It significantly increases GPU-GPU communication bandwidth and improves error detection and recovery features.

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Compatibility with NVIDIA Magnum IO and Mellanox Solutions:

The A100 is fully compatible with these solutions, maximizing I/O performance for multi-GPU multi-node accelerated systems and facilitating a broad range of workloads.

PCIe Gen 4 Support with SR-IOV:

By supporting PCIe Gen 4, the A100 doubles the PCIe 3.0/3.1 bandwidth, which benefits connections to modern CPUs and fast network interfaces. It also supports single root input/output virtualization, allowing for shared and virtualized PCIe connections for multiple processes or virtual machines.

Asynchronous Copy and Barrier Features:

The A100 includes new asynchronous copy and barrier instructions that optimize data transfers and synchronization and reduce power consumption. These features improve the efficiency of data movement and overlap with computations.

Task Graph Acceleration:

CUDA task graphs in the A100 enable a more efficient model for submitting work to the GPU, improving application efficiency and performance.

Enhanced HBM2 DRAM Subsystem:

The A100 continues to advance the performance and capacity of HBM2 memory technology, which is essential for growing HPC, AI, and analytics datasets.

The NVIDIA A100, with its Ampere architecture, represents a sophisticated and powerful GPU solution tailored to meet the demanding requirements of modern AI, HPC, and data analytics applications.

How much faster is H100 vs A100?


The H100 GPU is up to nine times faster for AI training and thirty times faster for inference than the A100. The NVIDIA H100 80GB SXM5 is two times faster than the NVIDIA A100 80GB SXM4 when running FlashAttention-2 training.


NVIDIA H100's Hopper Architecture

NVIDIA's H100 leverages the innovative Hopper architecture, explicitly designed for AI and HPC workloads. This architecture is characterized by its focus on efficiency and high performance in AI applications. Key features of the Hopper architecture include:

Fourth-Generation Tensor Cores:

Delivering up to 6x faster performance than the previous generation, these cores are optimized for matrix operations crucial to AI computations.

Transformer Engine:

This dedicated engine accelerates AI training and inference, offering significant speed-ups in large language model processing.

HBM3 Memory:

The H100 is the first GPU with HBM3 memory, doubling bandwidth and enhancing performance.

Enhanced Processing Rates:

The H100 delivers robust computational power with 3x faster IEEE FP64 and FP32 rates than its predecessor.

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DPX Instructions:

These new instructions boost the performance of dynamic programming algorithms, which are essential for applications in genomics and robotics.

Multi-Instance GPU Technology:

This second-generation technology secures and efficiently partitions the GPU, catering to diverse workload requirements.

Advanced Interconnect Technologies:

The H100 incorporates fourth-generation NVIDIA NVLink and NVSwitch, ensuring superior connectivity and bandwidth in multi-GPU setups. Asynchronous Execution and Thread Block Clusters: These features optimize data processing efficiency, which is crucial for complex computational tasks.

Distributed Shared Memory:

Facilitating efficient data exchange between SMs, this feature enhances overall data processing speed.

The H100, with its Hopper architecture, marks a significant advancement in GPU technology. It reflects the continuous evolution of hardware designed to meet the growing demands of AI and HPC applications.

Performance benchmarks

Performance benchmarks can provide valuable insights into the capabilities of GPU accelerators like NVIDIA's A100 and H100. These benchmarks, which include Floating-Point Operations Per Second (FLOPS) for different precisions and AI-specific metrics, can help us understand where each GPU excels, particularly in real-world applications such as scientific research, AI modeling, and graphics rendering.

NVIDIA A100 performance benchmarks

NVIDIA's A100 GPU delivers impressive performance across a variety of benchmarks. In terms of Floating-Point Operations, the A100 provides up to 19.5 teraflops (TFLOPS) for double-precision (FP64) and up to 39.5 TFLOPS for single-precision (FP32) operations. This high computational throughput is essential for HPC workloads, such as scientific simulations and data analysis, where high precision is required.

Moreover, the A100 excels in tensor operations, which are crucial for AI computations. The tensor cores deliver up to 312 TFLOPS for FP16 precision and 156 TFLOPS for tensor float 32 (TF32) operations. This makes the A100 a formidable tool for AI modeling and deep learning tasks, which often require large-scale matrix operations and benefit from the acceleration provided by tensor cores.

Form FactorSXMSXM
FP64 Tensor Core19.5 TFLOPS67 TFLOPS
TF32 Tensor Core312 TFLOPS989 TFLOPS
BFLOAT16 Tensor Core624 TFLOPS1,979 TFLOPS
FP16 Tensor Core624 TFLOPS1,979 TFLOPS
FP8 Tensor CoreNot applicable3,958 TFLOPS
INT8 Tensor Core1248 TOPS3,958 TOPS
GPU Memory80 GB HBM2e80 GB
GPU Memory Bandwidth2,039 Gbps3.35 Tbps
Max Thermal Design Power400WUp to 700W (configurable)
Multi-Instance GPUsUp to 7 MIGs @ 10 GBUp to 7 MIGs @ 10 GB each
InterconnectNVLink: 600 GB/sNVLink: 900GB/s
PCIe Gen4: 64 GB/sPCIe Gen5: 128GB/s
Server OptionsNVIDIA HGX™ A100NVIDIA HGX H100
Partner and NVIDIA-Certified Systems with 4, 8, or 16 GPUsPartner and NVIDIA-Certified Systems™ with 4 or 8 GPUs
NVIDIA AI EnterpriseIncludedAdd-on

NVIDIA H100 performance benchmarks

The NVIDIA H100 GPU showcases exceptional performance in various benchmarks. In terms of Floating-Point Operations, while specific TFLOPS values for double-precision (FP64) and single-precision (FP32) are not provided here, the H100 is designed to significantly enhance computational throughput, essential for HPC applications like scientific simulations and data analytics.

Tensor operations are vital for AI computations, and the H100's fourth-generation Tensor Cores are expected to deliver substantial performance improvements over previous generations. These advancements make the H100 an extremely capable AI modeling and deep learning tool, benefiting from enhanced efficiency and speed in large-scale matrix operations and AI-specific tasks.

AI and Machine Learning capabilities

AI and machine learning capabilities are critical components of modern GPUs, with NVIDIA's A100 and H100 offering distinct features that enhance their performance in AI workloads.

Tensor Cores:

The NVIDIA A100 GPU, powered by the Ampere architecture, delivers significant AI and machine learning advancements. The A100 incorporates third-generation Tensor Cores, which provide up to 20X higher performance than NVIDIA's Volta architecture (the prior generation). These Tensor Cores support various mixed-precision computations, such as Tensor Float (TF32), enhancing AI model training and inference efficiency.

On the other hand, the NVIDIA H100 GPU also represents a significant leap in AI and HPC performance. It features new fourth-generation Tensor Cores, which are up to 6x faster than those in the A100. These cores deliver double the matrix multiply-accumulate (MMA) computational rates per SM compared to the A100 and even more significant gains when using the new FP8 data type. Additionally, H100's Tensor Cores are designed for a broader array of AI and HPC tasks and feature more efficient data management.

Multi-Instance GPU (MIG) Technology:

The A100 introduced MIG technology, allowing a single A100 GPU to be partitioned into as many as seven independent instances. This technology optimizes the utilization of GPU resources, enabling concurrent operation of multiple networks or applications on a single A100 GPU. The A100 40GB variant can allocate up to 5GB per MIG instance, while the 80GB variant doubles this capacity to 10GB per instance.

However, the H100 incorporates second-generation MIG technology, offering approximately 3x more compute capacity and nearly 2x more memory bandwidth per GPU instance than the A100. This advancement further enhances the utilization of GPU-accelerated infrastructure.

New Features in H100:

The H100 GPU includes a new transformer engine that uses FP8 and FP16 precisions to enhance AI training and inference, particularly for large language models. This engine can deliver up to 9x faster AI training and 30x faster AI inference speedups compared to the A100. The H100 also introduces DPX instructions, providing up to 7x faster performance for dynamic programming algorithms compared to the Ampere GPUs.

Collectively, these improvements provide the H100 with approximately 6x the peak compute throughput of the A100, marking a substantial advancement for demanding compute workloads. The NVIDIA A100 and H100 GPUs represent significant advancements in AI and machine learning capabilities, with each generation introducing innovative features like advanced Tensor Cores and MIG technology. The H100 builds upon the foundations laid by the A100's Ampere architecture, offering further enhancements in AI processing capabilities and overall performance.

Is the A100 or H100 worth purchasing?


Whether the A100 or H100 is worth purchasing depends on the user's specific needs. Both GPUs are highly suitable for high-performance computing (HPC) and artificial intelligence (AI) workloads. However, the H100 is significantly faster in AI training and inference tasks. While the H100 is more expensive, its superior speed might justify the cost for specific users.


Power efficiency and environmental impact

The Thermal Design Power (TDP) ratings of GPUs like NVIDIA's A100 and H100 provide valuable insights into their power consumption, which has implications for both performance and environmental impact.


The A100 GPU's TDP varies depending on the model. The standard A100 with 40 GB of HBM2 memory has a TDP of 250W. However, the SXM variant of the A100 has a higher TDP of 400W, which increases to 700W for the SXM variant with 80 GB memory. This indicates that the A100 requires a robust cooling solution and has considerable power consumption, which can vary based on the specific model and workload.

The TDP for the H100 PCIe version is 350W, which is close to the 300W TDP of its predecessor, the A100 80GB PCIe. The H100 SXM5, however, supports up to a 700W TDP. Despite this high TDP, the H100 GPUs are more power-effective than the A100 GPUs, with a 4x and nearly 3x increase in FP8 FLOPS/W over the A100 80GB PCIe and SXM4 predecessors, respectively. This suggests that while the H100 may have a high power consumption, it offers improved power efficiency compared to the A100, especially in terms of performance per watt.

Comparison of Power Efficiency:

While the A100 GPU operates at a lower power of 400 watts, it can go as low as 250 watts for some workloads, indicating better energy efficiency overall compared to the H100. The H100, on the other hand, is known for higher power consumption, which can reach up to 500 watts in certain scenarios. This comparison highlights that while both GPUs are robust and feature-rich, they differ significantly in their power consumption and efficiency, with the A100 being more energy-efficient overall.

While the NVIDIA A100 and H100 GPUs are both powerful and capable, they have different TDP and power efficiency profiles. The A100 varies in power consumption based on the model, but overall, it tends to be more energy-efficient. The H100, especially in its higher-end versions, has a higher TDP but offers improved performance per watt, especially in AI and deep learning tasks. These differences are essential to consider, particularly regarding environmental impact and the need for robust cooling solutions.

Whether you choose the A100's proven efficiency or the H100's advanced capabilities, we provide the resources you need for exceptional computing performance. Get started now!

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