NVIDIA GH200 Grace Hopper 480GB Superchip for Dell PowerEdge

The NVIDIA GH200 Grace Hopper Superchip for Dell PowerEdge represents a tightly integrated CPU+GPU architecture designed for the most demanding AI training, inference, and HPC workloads at scale. Built on NVIDIA's Grace Hopper architecture, this module combines the Grace Arm-based CPU with a Hopper-generation GPU die, connected via NVLink-C2C — a chip-to-chip interconnect delivering up to 900 GB/s of bidirectional bandwidth, effectively eliminating the traditional PCIe bottleneck between processor and accelerator.

The 480GB configuration refers to the combined memory capacity: the Hopper GPU die is paired with 96GB of HBM3 high-bandwidth memory, while the Grace CPU integrates 384GB of LPDDR5X memory, yielding a unified 480GB coherent memory space accessible by both compute elements. This coherent memory architecture is fundamental to workloads where large model weights, embeddings, or datasets must be shared fluidly between CPU and GPU without explicit data transfers, reducing latency and dramatically increasing effective throughput for large language models and scientific computing pipelines.

Deployed within Dell PowerEdge infrastructure, the GH200 Superchip targets enterprises building GPU-accelerated clusters for generative AI, deep learning model training, molecular dynamics simulation, climate modeling, and real-time inferencing at scale. The module carries NVIDIA part number 900-21010-0050-000 and is engineered for integration into Dell's validated PowerEdge server platforms, enabling enterprise IT teams to deploy tightly coupled, datacenter-grade AI infrastructure with confidence in hardware-software compatibility across the full stack.

Ideal for

• Large language model (LLM) training and fine-tuning requiring coherent high-bandwidth CPU+GPU memory access across hundreds of billions of parameters
• Generative AI inference serving for enterprise deployments where low-latency, high-throughput response to concurrent requests is critical
• High-performance computing (HPC) workloads such as molecular dynamics, climate simulation, and computational fluid dynamics that saturate traditional PCIe-attached GPU bandwidth
• Data analytics and in-database machine learning pipelines that benefit from unified CPU-GPU memory to process datasets exceeding conventional GPU VRAM limits
• Multi-modal AI model development combining vision, language, and structured data processing within a single coherent memory domain
• AI-driven scientific research platforms in pharmaceutical, energy, and materials science sectors requiring dense floating-point throughput and large working-set memory

Technical specifications

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