PCIe GPUs vs Baseboard GPUs: Understanding the Differences

PCIe GPUs vs Baseboard GPUs: Understanding the Differences

As Artificial Intelligence, deep learning, machine learning and large scale GPU computing continue to expand, one of the most common questions companies and individuals face is: Should we deploy PCIe or SXM Baseboard GPUs?
Both architectures are powerful, but their use cases defer - and choosing the right one directly impacts performance, scalability, cooling efficiency and ROI.

What Are PCIe GPUs

PCIe GPUs are graphics processors that are designed to be plugged in directly into a server's or workstation's PCI Express slots. These GPUs are extremely common as the technology has been around decades in workstations, gaming PCs, rackmount servers, and general-purpose compute nodes.
These types of GPUs are best for: gaming, AI inference servers, professional visualization, virtual machines, edge compute, and environments prioritizing flexibility and cost efficiency. As LLMs become more compact in size with different quantization levels, it becomes easier and easier everyday to run local AI models within your system with high caliber PCIe GPUs such as a the NVIDIA RTX PRO 4000/5000/6000 lineup. Need to run GPT's latest model, or DeepSeek's 1+ Billion parameter models, a PCIe GPU is the starter or common route to take before the enterprise route.

Pros & Cons of PCIe GPUs

• Widespread Compatibility (Supermicro, Asus, Gigabyte, Dell, HPE etc.)
• Easy Installation, replacement, and Upgrade.
• More affordable than SXM GPUs (PCIe GPUs more consumer based market)
• Ideal for AI inference, virtualization, and mixed workloads
• No proprietary baseboards required, just the correct PCIe generation of the motherboard should match the PCIe generation of the GPU for maximum performance.

• Reduced power envelope compared to SXM
• Limited Cooling capacity
• Weaker Multi-GPU communication (no NVLink/NVSwitch)
• Less efficient for training large AI models

What Are SXM Baseboard GPUs

SXM GPUs are NVIDIA's new high performance GPU modules mounted directly to a specialized baseboard called HGX Platform. These GPUs are used in advanced AI servers, multi-GPU clusters, supercomputers, and high-density training models. These are the GPUs that power and train the best AI models avaialble today. The more these GPUs increase in quality and quantity across the market the more upgraded AI models we will see. NVIDIA B200, B300, H100, H200 among other GPU models are very searched for in the enterprise level grade AI market, with 8U or bigger rackmount servers that use multiple redundant power supplies to power these monsters we call servers.
These GPUs excel at LLM training, deep learning research, high level matrix multiplication for neural networks, multi-GPU AI clusters, hyperscale data centersm and enterprise AI infrastructure.

Pros & Cons of SXM Baseboard GPUs

• Higher power limits (1000W+ depending on size and architecture of the system)
• As thermal temperatures are important, these GPUs have superior cooling, allowing a sustained peak performance
• NVLink, and NVSwitch support for ultra fast GPU-to-GPU connection to allow for communication withing a cluster of GPUs
• Bigger VRAM Values compared to PCIe GPUs
• Near-linear scaling in Multi-GPU AI training
• Optimal for LLMs, generative AI, HPC, and high level parallel computing

• Higher costs compared to PCIe GPUs
• Requirement of propietary HGX baseboards, leading to limited system compatibility
• More complex deployment and servicing

PCIe vs. SXM: Which to choose for AI

When building AI infrastructure or scaling GPU compute, the right choice depends on the workload and budget. SXM will be used for maximum AI training performance, multi-GPU scaling, and enterprise LLM development. Choosing PCIe GPUs will allow flexible deployments, inference servers, virtualization, and cost-efficient GPU acceleration. In conclusion, both architectures are powerful - but understanding their strengths ensures that you invest in the right GPU infrastructure and platform for your data-center, AI homelab or AI pipeline.