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Published on 14 Feb 2024

Comparing NVIDIA H100 PCIe vs SXM: Performance, Use Cases and More



Updated: 24 Jun 2024

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NVIDIA’s H100 GPU represents a massive generational leap in AI acceleration and performance. Built on the Hopper architecture, it beat all the records for inference speeds and supercomputing benchmarks upon its release. Businesses now have an amazing opportunity to tap into groundbreaking capabilities to enhance their deep learning workloads.

However, effectively leveraging the NVIDIA H100 requires evaluating the PCIe and SXM module form factors to match business infrastructure and performance demands. The H100 PCIe GPU plugs into standard PCIe slots, providing strong performance in cost-effective servers. Meanwhile, the SXM model offers NVLink technology providing significantly higher interconnect bandwidth compared to the PCIe. 

Understanding the NVIDIA H100 form factors is key to boosting AI initiatives with optimal servers. In this blog, we will bring out the comparison between the NVIDIA PCIe vs SXM H100 GPUs across compatibility, performance density, efficiency and cost considerations.

H100 PCIe vs SXM: Comparison Chart


H100 PCIe

H100 SXM


Standard Servers

High-Performance Computing Servers


80 GB HBM2e

80 GB HBM3

GPU Memory Bandwidth



Power Consumption (TDP)


Up to 700W

Target Applications

High-Throughput Data Analytics, Medical Imaging and Diagnosis, Interactive Design and Visualisation

Large-Scale HPC Simulations, AI Model Training on Massive Datasets, Drug Discovery and Materials Science

What is PCIe?

PCIe (Peripheral Component Interconnect Express) is a high-speed serial computer expansion bus standard designed to replace the older PCI, PCI-X, and AGP standards. It is commonly used for connecting high-speed components like graphics cards, solid-state drives (SSDs), and network interfaces to the motherboard of a computer. PCIe is a point-to-point connection, meaning each device connected to the bus has its dedicated connection to the host, allowing for higher performance compared to shared bus architectures. The standard is continually evolving, with PCIe 4.0 and 5.0 offering significantly increased data transfer rates compared to earlier versions. Its scalability, higher bandwidth, and improved efficiency make PCIe a fundamental technology in modern computing for both consumer and enterprise applications.

Hyperstack's NVIDIA H100 GPU with a PCIe Gen 5.0 form factor includes the following units:

  • 7 or 8 GPCs, 57 TPCs, 2 SMs/TPC, 114 SMs per GPU

  • 128 FP32 CUDA Cores/SM, 14592 FP32 CUDA Cores per GPU

  • 4 Fourth-generation Tensor Cores per SM, 456 per GPU

  • 80 GB HBM2e, 5 HBM2e stacks, 10 512-bit Memory Controllers

  • 50 MB L2 Cache

  • Fourth-Generation NVLink and PCIe Gen 5

What is SXM?

SXM Technology is a form factor and interconnects standard primarily used for high-performance GPUs in data centres and AI applications. Unlike traditional GPUs that connect to a motherboard via PCIe slots, SXM GPUs are directly socketed onto the motherboard, allowing for more direct and high-bandwidth connections. This design enables better power delivery and cooling solutions, which are critical for high-end GPUs, especially in dense server environments. The SXM standard is often associated with NVIDIA's A100 series GPUs, designed for deep learning and high-performance computing tasks. SXM modules are key in environments where computational power, energy efficiency, and data throughput are critical, such as in AI training and inference, scientific simulations, and large-scale data analytics.

The NVIDIA H100 GPU with SXM form factor includes the following units:

  • 8 GPCs, 66 TPCs, 2 SMs/TPC, 132 SMs per GPU

  • 128 FP32 CUDA Cores per SM, 16896 FP32 CUDA Cores per GPU

  • 4 Fourth-generation Tensor Cores per SM, 528 per GPU

  • 80 GB HBM3, 5 HBM3 stacks, 10 512-bit Memory Controllers

  • 50 MB L2 Cache

  • Fourth-Generation NVLink and PCIe Gen 5

H100 Performance Metrics: PCIe vs SXM

The NVIDIA H100 GPUs, both the PCIe and SXM5 versions, showcase significant advancements in various performance metrics compared to their predecessors and other GPUs on the market.

Graph Infographics (1)

Source: NVIDIA

Computing Power

As HPC, AI, and data analytics datasets continue to grow in size, and computing problems get increasingly more complex, greater GPU memory capacity and bandwidth are a necessity. The NVIDIA P100 was the world’s first GPU architecture to support the high-bandwidth HBM2 memory technology and the NVIDIA V100 provided an even faster, more efficient, and higher-capacity HBM2 implementation. The NVIDIA A100 GPU further increased HBM2 performance and capacity.

The NVIDIA H100 SXM5 GPU raises the bar considerably by supporting 80 GB (five stacks) of fast HBM3 memory, delivering over 3 TB/sec of memory bandwidth, effectively a 2x increase over the memory bandwidth of the A100 that was launched just two years before. The NVIDIA H100 PCIe provides 80 GB of fast HBM2e with over 2 TB/sec of memory bandwidth.

Bandwidth and Data Transfer Speeds

Both NVIDIA H100 GPUs have seen a significant upgrade in-memory capabilities. The SXM5 variant uses HBM3 memory, while the PCIe version uses HBM2. The NVIDIA H100 SXM5 offers a memory bandwidth of 1,920 GB/s, and the PCIe version offers 1,280 GB/s. The NVIDIA H100 models benefit from updated NVIDIA NVLink and NVSwitch technology, which provide increased throughput in multi-GPU setups​​​​.

Energy Efficiency and Power Consumption

The NVIDIA H100 GPUs are more energy-efficient compared to their predecessors. The H100 PCIe model has a thermal design power (TDP) of 350W, close to the A100 80GB PCIe's 300W. The SXM5 variant supports up to a 700W TDP. Despite the high power consumption, the NVIDIA H100 cards are more power-effective than NVIDIA A100 GPUs. For instance, the NVIDIA  H100 PCIe model achieves 8.6 FP8/FP16 TFLOPS/W, significantly higher than the A100's performance​​.

Target Applications: H100 PCIe vs SXM

The NVIDIA H100 is a high-performance accelerator designed for demanding AI, scientific computing, and data analytics workloads. It boasts the fourth-generation NVIDIA Tensor Core architecture, offering significant performance improvements over its predecessors. Here are some key target applications of the NVIDIA H100:

  • High-Performance Computing (HPC): Scientific simulations, weather forecasting, drug discovery, materials science, and engineering simulations.

  • Artificial Intelligence (AI): Machine learning training and inference, natural language processing, computer vision, robotics, and autonomous vehicles.

  • Data Analytics: Big data processing, real-time analytics, fraud detection, and personalised recommendations.

  • Content Creation and Design: 3D rendering, animation, video editing, virtual reality, and augmented reality.

Use Cases: H100 PCIe vs SXM

Here are the use cases of the NVIDIA H100 PCle and NVIDIA H100 SXM:



High-Throughput Data Analytics

Large-Scale HPC Simulations

Medical Imaging and Diagnosis

AI Foundational Model Training

Interactive Design and Visualisation

Drug Discovery and Materials Science


  1. High-Throughput Data Analytics: This technology is well-suited for processing massive datasets in real time, which is essential for detecting fraud, identifying anomalies, and offering personalised recommendations. The PCIe interface supports high-speed data transfer, which is crucial for these tasks.

  2. Medical Imaging and Diagnosis: Analysing medical images and videos requires both speed and accuracy, which H100  PCIe can provide. The high throughput of the H100 PCIe helps in processing large medical datasets quickly, leading to faster and more precise diagnoses.

  3. Interactive Design and Visualisation: For real-time rendering of complex 3D models and simulations, H100  PCIe's fast data transfer rates are beneficial. This can be particularly useful in design and engineering applications where immediate visual feedback is necessary.


  1. Large-Scale HPC Simulations: The SXM H100 is designed for running complex scientific and engineering simulations that demand massive computational power and memory bandwidth. This is because the SXM form factor allows for more direct communication with the CPU and other GPUs, which is ideal for high-performance computing (HPC) workloads.

  2. AI Model Training on Massive Datasets: Training complex AI models like large language models requires significant computational resources, which the SXM H100 can provide. The direct CPU-GPU interconnects in the SXM form factor can help speed up the training process by improving data transfer rates and reducing latency.

  3. Drug Discovery and Materials Science: The SXM H100 can accelerate the discovery of new drugs and materials through high-throughput simulations. These tasks often involve processing vast amounts of data and running complex algorithms that can benefit from the SXM H100's enhanced computational capabilities and memory bandwidth.

Future Outlook for NVIDIA H100 PCIe vs SXM

The NVIDIA H100 PCIe model will see widespread adoption across a range of mid to large-scale AI teams, showing unprecedented performance in cost-effective PCIe infrastructure. Its high availability and approachable total cost of ownership will speed the progress of pioneering AI initiatives. We can expect the PCIe H100 in the servers and workstations of numerous innovative companies applying deep learning to advance their industries.

Meanwhile, leading enterprises are expected to turn to NVIDIA H100 SXM5's extreme scalability, allowing divisions of NVIDIA H100 GPUs tightly interlinked via NVLink and NVSwitch. Large-scale businesses will utilise SXM’s 700W TDP in neural architecture search, generative AI, and multimodal perception models. To facilitate the diverse range of environments that AI workloads require, NexGen Cloud is building its Supercloud SXM environment offering users dedicated “AI Factories” for large-scale AI training. This will allow users to scale and switch between optimised environments in the most efficient way possible.

We can expect the NVIDIA H100 in PCIe and SXM forms to become the top AI accelerator to satisfy growing demands for performance while boosting both cost-conscious and leading-edge initiatives in deep learning innovation.

Which is Better: H100 PCIe vs SXM

Both the NVIDIA H100 PCIe and SXM form factors offer distinct advantages for AI workloads. The PCIe variant provides flexibility, easy installation and leverages existing server infrastructure, best for mainstream AI applications. The SXM5 H100 is designed for extensive AI and HPC environments with extreme multi-petaflop performance density. The choice between H100 PCIe and SXM5 depends on your specific workflow requirements. For AI development and inferencing at a moderate scale, the NVIDIA H100 PCIe offers excellent value. However, we recommend  NVIDIA H100 SXM for running the most demanding models and datasets.

Similar Read: Evaluating Performance and Cost-Efficiency: NVIDIA A6000 vs A100 Across Various Workloads


What is the NVIDIA H100 used for?

The NVIDIA H100 can be used for a variety of AI, HPC, Data Analytics and Rendering workloads. The NVIDIA H100 can be best used to train large language models (LLMs), which are Generative AI models that can generate text, translate languages and interact with humans, for example, Chat GPT.

What is NVIDIA SXM H100?

The NVIDIA SXM H100 is an accelerator module based on the new Hopper architecture and SXM form factor. It is designed to provide advanced AI capabilities and high performance for data centres. Key features of NVIDIA SXM H100 include:

  • 80GB HBM3 memory with 3TB/s bandwidth

  • Fourth-Generation NVLink and PCIe Gen 5

  • Second-Generation Multi-Instance GPU (MIG)

What are the advantages of NVIDIA SXM H100 over PCIe?

The NVIDIA SXM H100 delivers unparalleled multi-petaflop performance density tailored for cutting-edge AI and HPC scenarios. With a robust 700W TDP, NVSwitch connectivity, and hot-swappable modules, it excels in large-scale distributed training environments. While the NVIDIA H100 PCIe offers excellent value for moderate-scale AI tasks, the NVIDIA SXM H100 stands out for companies tackling the most demanding models and datasets. 

What is H100 power consumption?

The H100 power consumption for NVIDIA PCIe is up to 350W. For NVIDIA H100 SXM  it is up to 700W.

Hyperstack is introducing the most advanced AI clusters of their kind through the AI Supercloud, offering the NVIDIA HGX SXM5 H100 - built on custom DGX reference architecture. Deploy from 8 to 16,384 cards in a single cluster - only available through Hyperstack's Supercloud reservation - Reserve now!

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