Dell PowerEdge XE7745 vs XE9680
Dell PowerEdge XE7745
Dell PowerEdge XE9680
The PowerEdge XE7745 and XE9680 are both Dell accelerated servers for AI, but they sit at opposite ends of the workload. The XE7745 is a 4U, air-cooled, AMD EPYC platform built around flexible PCIe GPUs for inference, model fine-tuning, and enterprise AI that has to fit standard data-center racks. The XE9680 is Dell's flagship training server, a dense system of eight SXM GPUs wired together with all-to-all NVLink for foundation-model training and the heaviest generative AI. Match the XE7745 to serving and mixed acceleration, match the XE9680 to large-scale training, and the choice gets simple.
Side by side
| Dell PowerEdge XE7745 | Dell PowerEdge XE9680 | |
|---|---|---|
| Primary role | AI inference, model fine-tuning, RAG, and enterprise acceleration. Built to serve models efficiently and run mixed GPU workloads. | Large-scale AI training and heavy generative AI. Dell's flagship for foundation-model and multi-node training runs. |
| GPU architecture | Flexible PCIe GPUs, supporting up to eight double-wide accelerators or a larger count of single-wide cards. Mix and match to the workload. | Eight fully interconnected SXM GPUs on an NVIDIA HGX (or AMD Instinct) baseboard. A fixed, maximum-density accelerator complex. |
| GPU interconnect | PCIe Gen5 with optional NVLink bridges between GPU pairs. Ideal when work parallelizes across independent GPUs or small groups. | All-to-all NVLink and NVSwitch fabric, so all eight GPUs share memory bandwidth as one unit. Critical for large models that exchange gradients constantly. |
| Chassis and cooling | 4U, air-cooled, designed to drop into standard enterprise racks without exotic facilities. | 6U, with air-cooled and liquid-cooled (XE9680L) options for the densest, highest-power configurations. |
| CPU platform | AMD EPYC processors, with high core density and abundant PCIe lanes to feed many accelerators. | Dual-socket Intel Xeon Scalable, tuned to keep an eight-GPU training node fed at full tilt. |
| Ideal workloads | Inference serving, LLM fine-tuning, RAG pipelines, computer vision, digital twin and physical-AI simulation, and edge or enterprise AI. | Pretraining and full fine-tuning of large models, dense generative AI, and GPU clusters scaled across many nodes. |
| Scale-out fabric | Supports high-speed back-end networking, but usually deployed as standalone or small-cluster inference nodes. | Engineered as the building block of large GPU superclusters, with dedicated east-west fabrics for distributed training. |
| Facilities and investment | Lower power and cooling envelope and a lower entry point. The efficient way to add accelerated capacity broadly. | Premium investment with higher power, cooling, and networking demands, justified when training throughput is the goal. |
Shop these now
Live configurations from our catalog with partner pricing. Add to your cart to request a firm quote, or build a full BOM.
Dell PowerEdge XE7745
Dell PowerEdge XE9680
Choose Dell PowerEdge XE7745 when
The job is running models, not building them. The XE7745 is the better fit for inference at scale, LLM fine-tuning, retrieval-augmented generation, computer vision, and physical-AI or digital-twin simulation. Its flexible PCIe GPU layout lets you match accelerators to the workload and pack many independent GPUs into one node, which is exactly what high-throughput inference wants. Because it is 4U and air-cooled, it fits standard enterprise data centers without liquid cooling or special power provisioning, so it is the practical choice for distributed inference, edge sites, and teams standardizing on AMD EPYC. When the priority is serving AI efficiently and keeping facilities simple, the XE7745 is the pick. Build the configuration on /bom and send it to /quote.
Choose Dell PowerEdge XE9680 when
You are training, or the model has to span all eight GPUs as one unit. The 8-way SXM design with all-to-all NVLink and NVSwitch gives the shared, high-bandwidth GPU memory that foundation-model training and heavy generative AI depend on. It is Dell's flagship building block for GPU superclusters, so it is the right platform when you are scaling training across many nodes with a dedicated back-end fabric. The liquid-cooled XE9680L variant pushes density and power even further for the most demanding runs. Reach for it when raw training throughput, not facilities simplicity, sets the requirement. Spec the training node on /bom, then send it to /quote.
There is no single winner here, because these two servers answer different questions. The XE7745 is the inference and enterprise-AI workhorse: flexible PCIe GPUs, a 4U air-cooled chassis, and an efficient power profile that fits standard data centers, which makes it the default for serving, fine-tuning, and physical-AI workloads. The XE9680 is the training flagship: eight NVLink-connected SXM GPUs built to move as one unit and engineered to cluster across nodes for the largest models. As an authorized Dell partner, Uniqcli qualifies the deal on three questions. Are you training or serving? Does the model need all-to-all GPU bandwidth, or does it parallelize across independent GPUs? And what power and cooling can the facility support? Many AI programs end up running both, XE9680 nodes to train and XE7745 nodes to serve. Build either configuration on /bom and send it to /quote, or reach us through /contact to size the full deployment.
Talk to a specialistFrequently asked
What is the real difference between the XE7745 and XE9680?
It comes down to training versus inference and how the GPUs connect. The XE9680 uses eight SXM GPUs on an HGX baseboard with all-to-all NVLink, so every GPU shares memory bandwidth as one large accelerator, which is what large-scale training needs. The XE7745 uses flexible PCIe GPUs in a 4U air-cooled chassis, which is ideal for inference, fine-tuning, and running many independent GPU workloads. Match the platform to the job rather than to raw GPU count.
Can the XE7745 handle AI training?
Yes, for smaller models, fine-tuning, and jobs that parallelize across independent GPUs, the XE7745 is very capable. What it does not provide is the all-to-all NVLink fabric of the XE9680, which large foundation-model training relies on to keep eight GPUs working as a single unit. If your training runs are large and communication-bound, the XE9680 is the right platform. If they are moderate, or you are mainly fine-tuning and serving, the XE7745 often delivers better value.
Do I need liquid cooling for either server?
The XE7745 is air-cooled and designed to fit standard enterprise racks, so no special cooling is required. The XE9680 is available air-cooled, and the XE9680L variant adds liquid cooling for the densest, highest-power training configurations. Facilities capacity is one of the first things to confirm, and Uniqcli can review power and cooling with you when you build the configuration on /bom.
Which is better for deploying inference across many sites?
The XE7745. Its 4U air-cooled design, flexible PCIe GPUs, and lower power envelope make it well suited to distributed inference, edge locations, and repeatable enterprise rollouts. The XE9680 is overbuilt for most standalone inference nodes, since its strengths only pay off when training throughput or all-to-all GPU bandwidth is the requirement.
Are both available on federal contracts?
Yes. Both are Dell PowerEdge servers that Uniqcli can supply as TAA-compliant configurations through federal vehicles including NASA SEWP V and GSA, and via GPC where applicable. Whether you are standing up a training cluster on XE9680 or an inference fleet on XE7745, send the requirement to /quote or reach us at /contact and we will scope a compliant build on /bom.
Build your Dell bill of materials.
Send us the requirement, the project, or an existing quote to beat. We come back with a validated, TAA-compliant Dell configuration and a real price, often below list.
[email protected] · Chicago, IL