Dell's PowerEdge XE line is its purpose-built AI and GPU range, and choosing within it is really a question about your workload, not your budget. An eight-GPU training node, a balanced four-GPU box and a flexible PCIe-GPU platform solve very different problems, and buying the wrong one means either paying for interconnect you will never use or hitting a ceiling the moment you scale. This guide maps the XE9680, XE8640 and XE7745 to training, inference and mixed use, and explains the form-factor and fabric choices that decide which one is right.
The XE line in one view
The XE family separates by how many accelerators it holds and how they are connected. The XE9680 is the flagship eight-GPU training platform with SXM-class accelerators on a high-bandwidth NVLink baseboard; the XE8640 is a balanced four-GPU node, also with high-speed GPU-to-GPU interconnect, for smaller training and demanding inference; and the XE7745 is a flexible PCIe-GPU platform that takes a range of accelerators for inference and mixed workloads. The differences in GPU count, interconnect and power are what you are actually choosing between.
Frame the decision by workload first. Large-model training that must keep many accelerators fed as one tightly-coupled unit points to the XE9680. Smaller training runs and high-throughput inference fit the XE8640. Inference at scale, fine-tuning, and mixed estates that value flexibility over peak interconnect bandwidth suit the XE7745. Our Dell PowerEdge XE hub covers the range, and our GPU accelerators guidance the silicon that goes in them.
SXM vs PCIe: the choice that shapes everything
The single most important distinction across the XE line is SXM versus PCIe accelerators. SXM modules, as in the XE9680 and XE8640, sit on a shared baseboard with very high-bandwidth NVLink between GPUs, which is what makes large-model training scale across eight accelerators without the interconnect becoming the bottleneck. They also draw more power and need the cooling to match. PCIe GPUs, as in the XE7745, are more flexible and easier to mix and match, at the cost of the all-to-all GPU bandwidth that SXM provides.
The practical rule: if your workload is one large model trained across many GPUs that must exchange data constantly, SXM and NVLink earn their premium. If your workload is many independent inference or fine-tuning jobs, PCIe GPUs give you flexibility and better cost-per-accelerator without leaving performance on the table. We unpack this in detail in SXM vs PCIe GPUs and EDSFF explained.
Feeding the accelerators: NVMe, NICs and fabric
GPUs are only as useful as the data you can keep flowing to them. An XE node needs fast local NVMe for staging and checkpoints, and high-speed networking sized so the fabric, not the accelerators, sets the pace. For multi-node training the cluster fabric matters as much as the server: high-bandwidth NICs and a low-latency network are what let many nodes act as one. Read building your first UK on-prem AI cluster for how the nodes, fabric and storage fit together.
Storage feeds training and inference differently. Training wants high-throughput streaming and fast checkpointing; inference wants low-latency access to model weights. Size local NVMe and the path to shared storage for the dominant pattern, using our SSD and NVMe range, so the expensive accelerators are never idle waiting on I/O.
Power and cooling are part of the spec
An eight-GPU SXM node draws far more power and rejects far more heat than a conventional server, and that is a facilities decision as much as a hardware one. Before committing to an XE9680, confirm the rack power budget, the power distribution and the cooling approach, because a dense GPU node in a rack that cannot feed or cool it is a stranded asset. The XE8640 and PCIe-based XE7745 are less demanding but still warrant a power and cooling check.
This is why GPU server selection should start with the facility as well as the workload. We design the node, the rack power and the cooling together rather than in isolation, and for the wider context on AI thermals see liquid vs immersion cooling and RoCE explained. Getting power and cooling right is what turns an expensive GPU server into a productive one.
Matching chassis to use case
Put together, the mapping is straightforward. Large-model training that needs eight tightly-coupled accelerators points to the XE9680. Balanced four-GPU training and demanding inference fit the XE8640. Inference at scale, fine-tuning and mixed workloads that value flexible PCIe accelerators suit the XE7745. Many organisations run a mix: a small number of training nodes alongside more numerous inference nodes, sized to their actual job profile.
Against a turnkey appliance, the XE line gives you an OEM-supported, standards-based platform you can integrate into an existing estate. Build the exact configuration in our Dell configurator, compare the appliance route on the NVIDIA DGX page, and talk to us about sizing the fabric and facility around it.