NVIDIA GH200 Grace Hopper Superchip 480GB HBM3e

The NVIDIA GH200 Grace Hopper Superchip combines the NVIDIA Grace CPU and Hopper GPU architecture on a single module, interconnected via NVLink-C2C with 900 GB/s of chip-to-chip bandwidth. This unified memory architecture enables the CPU and GPU to share a coherent memory space, dramatically reducing data movement overhead for memory-intensive AI and HPC workloads. The 480 GB HBM3e configuration delivers up to 4.9 TB/s of GPU memory bandwidth, making it one of the highest-bandwidth compute platforms available for enterprise datacenter deployment.

Built on the Hopper GPU architecture, the GH200 incorporates fourth-generation Tensor Cores, second-generation Transformer Engine, and NVLink 4.0 fabric support for multi-GPU scaling. The Grace CPU component features 72 Arm Neoverse V2 cores and 480 GB of LPDDR5X system memory, providing a tightly integrated compute substrate well-suited to large-scale transformer model inference and training. The combination of HBM3e GPU memory and LPDDR5X CPU memory gives the full superchip 624 GB of total addressable coherent memory.

The GH200 480 GB HBM3e targets enterprises running generative AI model training, large language model inference, scientific simulation, and high-performance computing workloads at scale. It is designed for deployment in high-density datacenter nodes such as the NVIDIA MGX reference architecture and DGX GH200 systems, and supports NVLink Switch System configurations for multi-node GPU clusters. The module is qualified for demanding enterprise AI infrastructure requiring maximum memory capacity, bandwidth, and compute density in a single accelerator package.

Ideal for

• Large language model training and fine-tuning for enterprise AI platforms requiring models exceeding 70 billion parameters
• Generative AI inference serving for production deployments where large model weights must reside entirely in accelerator memory
• High-performance computing simulations in computational fluid dynamics, molecular dynamics, and climate modeling demanding extreme memory bandwidth
• Recommender system training on massive embedding tables that benefit from the unified 624 GB coherent CPU and GPU memory space
• Multi-modal AI workload development combining vision, language, and structured data models within a single high-bandwidth memory domain
• Sovereign AI datacenter buildouts across UAE, GCC, EMEA, and APAC requiring dense, scalable GPU compute infrastructure

Technical specifications

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