NVIDIA A100 PCIe vs NVIDIA A100 SXM: A Comprehensive Comparison

The NVIDIA A100 is built on the powerful Ampere architecture to deliver groundbreaking performance for AI, machine learning and high-performance computing (HPC) workloads. With its innovative architecture design, the NVIDIA A100 offers accelerated performance for the most demanding tasks. The NVIDIA A100 GPU comes with two configurations- PCIe and SXM. The goal behind offering different configurations is to cater to a wide range of use cases, from smaller-scale applications to large-scale AI model training. Read our full comparison below to see which NVIDIA A100 option is best suited for your needs. 

What is PCIe? 

PCIe (Peripheral Component Interconnect Express) is an industry-standard interface that connects components like GPUs, SSDs and network cards to the motherboard. It provides high-speed communication between the CPU and GPU for efficient data transfer. 

Key Features of NVIDIA A100 PCIe 

The key features of NVIDIA A100 PCIe GPU include: 

• Scalability: PCIe supports server configurations ranging from single-GPU setups to systems with up to eight GPUs. 

• Interconnect Bandwidth: Using PCIe Gen4, the NVIDIA A100 PCIe delivers a bandwidth of up to 64 GB/s, enabling fast communication between the CPU and GPU. 

• Cooling Options: A100 PCIe GPUs come in dual-slot, air-cooled, or liquid-cooled configurations, making them adaptable to various environments. 

• Flexibility: PCIe GPUs can be used across a broad range of servers, ensuring compatibility with existing hardware infrastructure. 

What is SXM? 

SXM (Server PCI Express Module) is a proprietary NVIDIA form factor designed for enterprise-grade workloads and high-performance data centre deployments. Unlike PCIe GPUs, SXM GPUs are integrated directly onto the server board via NVIDIA HGX for advanced capabilities. 

Key Features of NVIDIA A100 SXM 

The key features of NVIDIA A100 SXM GPU include: 

• NVLink Integration: SXM GPUs leverage NVIDIA NVLink technology, enabling GPU-to-GPU communication bandwidth of up to 600 GB/s- almost 10x faster than PCIe Gen4. 

• Optimised Form Factor: The SXM module is designed for dense data centres, supporting up to 400W TDP to deliver peak performance. 

• Thermal Efficiency: Advanced cooling mechanisms allow SXM GPUs to handle heavy workloads without overheating. 

• Enterprise-Ready: Often used in NVIDIA DGX systems, SXM GPUs are tailored for HPC, AI training, and large-scale simulations. 

Performance Metrics: NVIDIA A100 PCIe vs NVIDIA A100 80GB SXM4

As we can see from the metrics below, the NVIDIA A100 SXM configuration outperforms NVIDIA A100 PCIe in every metric, particularly in Tensor Core throughput and memory bandwidth, making it the preferred choice for computationally intensive tasks like large-scale deep learning. However, it's worth noting that the NVIDIA A100 SXM also requires more power with a TDP of 400W, compared to the 300W TDP of the NVIDIA A100 PCIe. While the SXM offers superior performance, the PCIe version is more energy efficient. 

Use Cases: NVIDIA A100 PCIe vs SXM GPU 

Check out a detailed comparison of use cases below for NVIDIA A100 PCIe and NVIDIA A100 SXM: 

Deep Learning Training 

The NVIDIA A100 PCIe offers adequate performance for smaller deep learning models and less demanding tasks. It supports moderate training speeds, making it suitable for workloads that don’t require extreme interconnect bandwidth. However, for large-scale model training, the NVIDIA A100 SXM shines. With NVLink's high-bandwidth GPU-to-GPU communication, the NVIDIA A100 SXM accelerates training by enabling faster data exchange between GPUs, drastically reducing training time. This is crucial for deep learning tasks involving large models, complex algorithms, or distributed computing. 

AI Inference 

Inference tasks typically require low-latency performance, especially in real-time applications. The NVIDIA A100 PCIe is suitable for smaller-scale inference tasks, providing efficient processing at lower costs. It is ideal for environments that do not need extensive parallel processing. On the other hand, the NVIDIA A100 SXM is optimised for dense inference tasks with its high throughput and reduced latency. Thanks to its NVLink and superior inter-GPU communication, the SXM excels in handling large-scale inference workloads, where fast and simultaneous processing across multiple models is essential. 

HPC Simulations 

The NVIDIA A100 PCIe is capable of supporting basic HPC simulations but may face limitations in scaling when dealing with high-fidelity simulations requiring multiple GPUs. It can handle moderately complex calculations and data processing in smaller scenarios. In contrast, the NVIDIA A100 SXM is built for high-performance computing at scale. With NVLink, SXM ensures seamless communication between GPUs, enabling large-scale simulations that require the combined power of multiple GPUs. This makes SXM the preferred choice for advanced scientific research and simulations in fields like physics, chemistry, and engineering. 

Data Analytics 

The NVIDIA A100 PCIe is well-suited for data analytics in smaller-scale environments, handling moderate datasets effectively. It supports tasks like ETL (Extract, Transform, Load), machine learning preprocessing, and basic analytics, delivering good performance for less intensive applications. However, when dealing with massive datasets or real-time analytics, the NVIDIA A100 SXM offers substantial advantages. Its high memory bandwidth and NVLink connectivity allow faster data processing and real-time insights, making it ideal for big data analytics in enterprise-scale environments. It excels in complex queries and machine learning model deployment across large datasets. 

Which is Better: NVIDIA A100 PCIe vs NVIDIA A100 SXM GPU? 

Choosing between PCIe and SXM largely depends on your workload requirements, budget and deployment environment. But if you want to learn which is better, you must: 

Go with NVIDIA A100 PCIe if: 

• You need a flexible GPU that can adapt to various server configurations. 

• Your workloads are not compute-heavy and do not require significant inter-GPU communication. 

• Budget constraints are a significant consideration for you. Our NVIDIA A100 PCIe. 

We offer NVIDIA A100 80GB PCIe and the NVIDIA A100 80B PCIe with NVLink, starting at just $0.95/hour. Try the NVIDIA A100 PCIe on Hyperstack today! 

Choose A100 SXM if: 

• You’re working on high-performance AI training, HPC, or advanced data analytics. 

• Your workloads involve multi-GPU configurations that demand maximum interconnect bandwidth. 

• You want to future-proof your data centre for enterprise-scale deployments. 

Both configurations deliver excellent performance, but the NVIDIA A100 SXM is the superior option for workloads demanding peak performance and scalability. 

Want to scale up your AI workloads? Reserve NVIDIA A100 SXM today for early access as we are launching NVIDIA A100 SXM4 GPUs this month! 

You May Also Like to Read: 

• 5 Real-World Use Cases of NVIDIA A100 GPUs 

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

• NVIDIA RTX A6000 vs NVIDIA A100 Across Various Workloads 

FAQs 

What is the main difference between NVIDIA A100 PCIe and NVIDIA A100 SXM? 

The NVIDIA A100 PCIe is a more flexible and cost-effective solution, while the A100 SXM offers superior performance with NVLink and higher inter-GPU bandwidth, making it ideal for large-scale, high-performance workloads. 

Which is better for deep learning training, NVIDIA A100 PCIe or NVIDIA A100 SXM? 

The NVIDIA A100 SXM is better for deep learning training, especially with large models, due to its high throughput and NVLink support for fast GPU-to-GPU communication. 

Is the NVIDIA A100 SXM energy efficient? 

The NVIDIA A100 PCIe is generally more energy-efficient with a lower power consumption of 300W compared to the 400W required by the NVIDIA A100 SXM. 

Is NVLink available on both NVIDIA A100 PCIe and NVIDIA A100 SXM? 

Yes, the NVLink is available for both the NVIDIA A100 PCIe and the NVIDIA A100 SXM, providing much higher GPU-to-GPU communication bandwidth compared to the PCIe configuration.