Dell EMC PowerEdge XE8545

The Dell EMC PowerEdge XE8545 is a purpose-built, 4U two-socket server designed to deliver exceptional GPU density and compute performance for high-performance computing and artificial intelligence workloads. It accommodates up to 8 double-wide or 16 single-wide GPU accelerators, paired with AMD EPYC 7002-series or newer processors, enabling simultaneous execution of compute-intensive training, inference, and simulation tasks at scale. The system features a modular GPU subsystem with hot-swappable GPU cartridges, allowing flexible configuration and simplified maintenance in production HPC clusters.

Architected with enterprise-grade reliability and thermal management, the XE8545 integrates redundant power supplies, comprehensive monitoring via Dell OpenManage Enterprise, and optimized airflow design to sustain continuous GPU utilization in demanding environments. The server supports up to 2TB of DDR4 memory per node, high-bandwidth InfiniBand or Ethernet networking options, and NVMe SSD storage for model weights, training datasets, and intermediate results. Its design prioritizes multi-GPU scaling efficiency and low-latency inter-node communication, critical for distributed deep learning frameworks and large-scale scientific simulations.

The platform is validated and optimized across leading AI frameworks including PyTorch, TensorFlow, and NVIDIA CUDA, with comprehensive driver and firmware support through Dell EMC's lifecycle management tools. This architecture supports organizations scaling from single-node model development to multi-node, multi-rack GPU clusters for production AI pipelines and HPC research environments.

Ideal for

• Large-scale deep learning model training for computer vision, natural language processing, and generative AI applications requiring multi-GPU parallelism
• High-performance computing simulations including computational fluid dynamics, molecular dynamics, and climate modeling with GPU-accelerated solvers
• Real-time AI inference serving with batched prediction workloads across multiple GPU instances for recommendation engines and vision systems
• Machine learning feature engineering and data preprocessing at scale, leveraging GPU acceleration for rapid ETL and statistical analysis
• Scientific research and academia: physics simulations, quantum chemistry calculations, and materials science modeling with GPU-optimized algorithms
• Enterprise AI model development pipelines integrating training, validation, and optimization workflows across shared HPC cluster infrastructure

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.