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.
PCIe GPUs are often recommended for budget constraints and moderate performance needs. Here's when and why you must choose PCIe GPUs over SXM:
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 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
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
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:
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:
Similar Read: Comparing NVIDIA H100 PCIe vs SXM
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:
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:
New 128 NVIDIA H100 SXM Systems are coming soon on Hyperstack.
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The decision between SXM and PCIe GPUs depends largely on your specific needs, budget and project scale.
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SXM GPUs offer superior performance, memory bandwidth, and scalability for large-scale projects, while PCIe GPUs are affordable and compatible for moderate workloads.
SXM GPUs such as the NVIDIA H100 SXM on Hyperstack are ideal due to their high-bandwidth NVLink interconnects and exceptional performance for distributed training.
Yes, PCIe GPUs deliver the necessary precision and flexibility for fine-tuning tasks. We offer NVIDIA A100 PCIe and NVIDIA H100 PCIe GPUs on Hyperstack, ideal for fine-tuning the latest LLMs like the Llama 3.
Yes, PCIe GPUs are capable of batch inference. However, due to lower latency and higher throughput, SXM GPUs are better suited for real-time, large-scale inference.
Hyperstack offers 1-click deployment so that you can deploy NVIDIA H100 SXM GPUs within minutes.
NVIDIA H100 SXM GPUs for reservation start at just $2.10/hr and on-demand pricing is $3.00/hr on Hyperstack.