When to Choose SXM Over PCIe GPUs for Your AI or HPC Projects

If you’re planning to deploy your first AI model or scale an existing project and getting confused in choosing the right GPU, you’re not alone. Both PCIe and SXM GPUs offer high-end performance, making the choice far from straightforward. SXM GPUs are often considered better due to their enhanced bandwidth and power efficiency, but do you know why they might be the right fit for your workload over PCIe GPUs? Hence, understanding the differences is imperative to make an informed decision.

In this blog, we explore the difference between PCIe and SXM GPUs. By the end of this read, you’ll have a clear idea of when to choose SXM GPUs over PCIe for your AI and HPC projects.

When to Choose PCIe GPUs

PCIe GPUs are often recommended for budget constraints and moderate performance needs. Here's when and why you must choose PCIe GPUs over SXM:

Image: NVIDIA A100 PCIe GPU

Fine-Tuning Large Language Models (LLMs)

Fine-tuning pre-trained language models like GPT, BERT, T5 or Llama don’t always require the extreme throughput provided by SXM GPUs. PCIe GPUs deliver the precision and performance necessary for fine-tuning. With the NVIDIA A100 PCIe GPU, for example, researchers and developers benefit from 432 third-generation Tensor Cores. These cores are designed to accelerate AI workloads, delivering faster processing for matrix multiplications and quicker iterations during fine-tuning tasks.

PCIe GPUs are also ideal for fine-tuning because they are compatible with diverse server architectures. The PCIe standard ensures GPUs can easily integrate into existing infrastructures without requiring specialised hardware configurations. This flexibility allows teams to achieve high efficiency in their workflows without incurring significant additional costs for system upgrades. 

Batch Inference for AI Applications

Batch inference is critical in AI applications such as recommendation engines, image recognition, and natural language processing (NLP), which demands efficient and consistent performance. PCIe GPUs like the NVIDIA H100 PCIe excel in handling batch workloads due to their exceptional computational capabilities and NUMA-aware scheduling. This feature ensures that memory-intensive inference tasks are optimally distributed across GPUs, reducing latency and enhancing throughput.

For example, in AI-driven recommendation systems, low-latency inference is most important. The NVIDIA H100 PCIe GPU with high-speed networking of up to 350 Gbps, data is exchanged between clusters with minimal delays to process numerous inference requests

HPC Workloads in Moderately Complex Research

For moderately complex research, PCIe GPUs offer an ideal balance of power and affordability. Tasks such as computational fluid dynamics, weather forecasting and finite element analysis could benefit from the FP64 precision capabilities of PCIe GPUs like the NVIDIA A100 PCIe and NVIDIA H100 PCIe. The NVIDIA A100 PCIe GPU delivers up to 19.5 TeraFLOPS of FP64 performance, ensuring precise calculations for data-intensive scientific tasks.

The NVIDIA H100 PCIe GPU offers significant improvements in HPC workloads. It delivers up to 7x higher performance for HPC tasks than its previous generation. The GPU's high memory bandwidth of 3.9TB/s and 60 teraflops of FP64 ensure that large datasets are processed efficiently, reducing bottlenecks and accelerating time-to-insight

But that’s not all, PCIe GPUs are also optimised for cost-effective scaling. Our NVIDIA A100 PCIe and NVIDIA H100 PCIe GPUs come with NVLink interconnects, which provide up to 600 GB/s bandwidth for GPU-to-GPU communication, making them ideal for parallel and distributed computing in research environments. 

Similar Read: NVIDIA A100 PCIe vs NVIDIA A100 SXM

When to Choose SXM GPUs

SXM GPUs are best for specialised, high-demand environments where cutting-edge performance and scalability are needed. Below are some use cases where you can choose SXM GPUs over PCIe:

Image: NVIDIA H100 SXM GPU

Large-Scale AI Model Training

For large-scale AI model training that requires high inter-GPU communication, the PCIe may become a bottleneck compared to NVLink or SXM which offer significantly higher bandwidth for GPU-to-GPU communication. Hence, SXM GPUs are ideal for training advanced AI models or LLMs like Meta’s Llama 3, OpenAI’s GPT or image generation models like Stable Diffusion, which require significant computational resources. SXM GPUs like the NVIDIA H100 SXM GPUs offer:

• 80GB of HBM3 Memory with a bandwidth of up to 3.6 TB/s provides the capacity to process massive datasets and intricate neural network architectures without memory bottlenecks. 
• 1,984 Tensor Cores accelerate mixed-precision training, enabling faster iterations and reduced time-to-solution for AI developers. 
• The NVLink interconnect delivers ultra-fast GPU-to-GPU communication at 600 GB/s, making distributed training seamless and efficient. Combined with the SXM5 Interconnect, which maximises throughput with exceptional power efficiency, these features drastically reduce training time for cutting-edge models. 

Similar Read: Comparing NVIDIA H100 PCIe vs SXM

Real-Time AI Inference at Scale

Performance and latency are important when deploying AI models for real-time applications such as autonomous vehicles, healthcare diagnostics or live video analytics. SXM GPUs like the NVIDIA H100 SXM excel in these scenarios by offering:

• 2,000 TOPS for AI Inference for rapid prediction and response times even at scale. 
• The NVLink interconnect for high-speed communication between GPUs, facilitating efficient distributed inference for complex multi-GPU setups. 
• With high-speed networking of up to 350 Gbps, data is exchanged between clusters with minimal delays, supporting large-scale deployments where low latency is critical. 
• Thermal optimisation to prevent throttling under heavy and continuous workload pressure, ensuring optimal performance during inference at scale.

High-Performance Computing (HPC)

High-performance computing applications such as molecular modelling, climate simulations and large-scale scientific research demand extreme computational precision and bandwidth. SXM GPUs like the NVIDIA H100 SXM GPUs provide: 

• 60 TeraFLOPS of FP64 performance, ensuring double-precision accuracy for intricate simulations and computational tasks. 
• The 80 GB of HBM3 Memory with ultra-high bandwidth facilitates the seamless processing of vast datasets, critical for tasks like modelling weather systems or running bioinformatics algorithms. With NVLink and NVSwitch, these GPUs enable efficient and full-mesh GPU-to-GPU connectivity to optimise workflows that require extensive parallel processing across multiple nodes. 
• High-speed networking of up to 350 Gbps ensures high-speed communication between distributed nodes, significantly reducing data transfer overheads in large-scale HPC environments. 

Coming Soon!

New 128 NVIDIA H100 SXM Systems are coming soon on Hyperstack.

Fully equipped to power large-scale AI training, real-time inference and HPC workloads.

Final Thoughts: Which GPU to Choose?

The decision between SXM and PCIe GPUs depends largely on your specific needs, budget and project scale.

• For Large-Scale AI and HPC: SXM GPUs like the NVIDIA H100 SXM are ideal for large-model AI training due to their high bandwidth, NVLink interconnects, and ability to scale across large clusters. Their specialised capabilities make them perfect for research labs, advanced AI model training, and large-scale simulations in data centres. At Hyperstack, we offer the NVIDIA H100 SXM, starting at just $2.10/hr. 
• For Cost-Conscious or Moderate Projects: PCIe GPUs are well-suited for small- to medium-scale workloads and general-purpose use cases like fine-tuning or experimenting with the latest LLMs. They offer broad compatibility, flexibility and affordability, making them ideal for prototyping, fine-tuning and moderate AI deployments. At Hyperstack, we offer NVIDIA H100 PCIe and NVIDIA A100 PCIe. Check out our cloud GPU pricing here!

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