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 A100 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.
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Metric
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NVIDIA A100 PCIe
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NVIDIA A100 80GB SXM4
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FP64 Performance
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9.7 TFLOPS
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9.7 TFLOPS
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Tensor Core FP16
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312 TFLOPS
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624 TFLOPS with sparsity
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Memory Bandwidth
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1,935 GB/s
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2,039 GB/s
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GPU to GPU bandwidth
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A100 PCIe: 64 GB/s (PCIe Gen4)
A100 PCIe NVLink: 600 GB/s (NVLink 12)
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600 GB/s (NVSwitch + NVLink 12)
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Power Consumption
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300W
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400W
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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:
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Use Case
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NVIDIA A100 PCIe
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NVIDIA A100 SXM
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Deep Learning Training
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Moderate throughput
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High throughput with NVLink
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Inference
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Lower latency environments
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Optimised for AI inference
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HPC Simulations
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Basic simulations
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Advanced multi-GPU configurations
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Data Analytics
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Entry-level workloads
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Large-scale datasets
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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!
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FAQs
What are the key differences in performance between A100 PCIe vs SXM?
When comparing A100 PCIe vs SXM, the SXM offers higher throughput, better thermal efficiency, and superior GPU-to-GPU communication via NVLink. The PCIe version is more cost-effective and flexible, making it suitable for smaller-scale AI training, inference, and data analytics workloads.
Why is the A100 SXM preferred for enterprise workloads?
The A100 SXM is designed for high-performance enterprise deployments. With NVLink bandwidth up to 600 GB/s and higher thermal limits (400W TDP), the SXM can handle large-scale AI training, HPC simulations, and big data analytics that require dense GPU clusters.
When should I choose the A100 PCIe?
The A100 PCIe is a great option if you need flexibility across multiple server types, lower power consumption (300W), or a cost-efficient GPU for moderate training and inference. It’s also available with NVLink configurations for enhanced interconnect bandwidth, making it versatile for scaling as needed.
What is an SXM GPU and how is it different from PCIe?
An SXM GPU is a proprietary NVIDIA form factor designed for direct integration into server boards with HGX. Unlike PCIe GPUs, SXM GPUs support higher power limits, advanced cooling, and NVLink bandwidth for dense multi-GPU configurations, making them ideal for large-scale AI and HPC.
What is A100 NVLink and why is it important?
A100 NVLink is NVIDIA’s high-bandwidth GPU interconnect that allows multiple GPUs to communicate directly at speeds up to 600 GB/s. This drastically reduces training time for large models by eliminating communication bottlenecks, which is especially useful for distributed deep learning.
What is the A100 memory bandwidth for PCIe and SXM?
The A100 memory bandwidth differs slightly across configurations: the A100 PCIe delivers up to 1,935 GB/s, while the A100 SXM4 reaches 2,039 GB/s. Higher memory bandwidth enables faster data access, critical for training massive AI models and processing large datasets.
How much power does the A100 consume?
A100 power consumption varies by configuration. The A100 PCIe operates at 300W, making it more energy-efficient, while the A100 SXM consumes 400W but delivers higher performance thanks to advanced cooling and increased throughput.
What is the A100 NVLink bandwidth compared to PCIe bandwidth?
The A100 NVLink bandwidth reaches up to 600 GB/s, nearly 10x faster than the 64 GB/s bandwidth provided by PCIe Gen4. This makes NVLink essential for workloads that require fast GPU-to-GPU communication, such as large-scale AI training and HPC simulations.
