NVIDIA GB200 Grace Blackwell Superchip NVL2 Module

The NVIDIA GB200 Grace Blackwell Superchip NVL2 Module (part number 900-23685-0000-000) represents a tightly integrated compute platform combining two NVIDIA Blackwell GPU dies with an NVIDIA Grace CPU via ultra-high-bandwidth NVLink-C2C interconnects. The Grace Blackwell Superchip architecture delivers a unified CPU+GPU memory space, enabling large AI models and HPC workloads to operate across a coherent 960 GB of total memory — comprising 192 GB of HBM3e GPU memory and up to 768 GB of LPDDR5X CPU memory — without the bottlenecks of traditional PCIe-attached configurations. The NVL2 module pairs two Grace Blackwell Superchips in a dual-module configuration designed for NVL72 rack-scale deployments.

At the GPU level, each Blackwell die delivers fourth-generation Tensor Core performance supporting FP4, FP8, FP16, BF16, TF32, and FP64 precisions, along with second-generation Transformer Engine acceleration. The GB200 introduces a new RAS (Reliability, Availability, and Serviceability) engine and row-remapping capabilities in HBM3e, reinforcing enterprise-grade resiliency expectations for 24/7 datacenter operations. NVLink-C2C provides 900 GB/s of bidirectional chip-to-chip bandwidth between the Grace CPU and the two Blackwell GPUs, eliminating traditional host-to-device memory copy overhead for large-scale model inference and training pipelines.

The GB200 NVL2 Module is engineered for deployment within NVIDIA NVL72 liquid-cooled rack systems, where up to 36 NVL2 modules interconnect via fifth-generation NVLink Switch fabric to form a single high-bandwidth compute domain. This architecture is purpose-built for trillion-parameter foundation model training, large-scale generative AI inference, scientific simulation, and sovereign AI infrastructure — making it a strategic platform for enterprises, hyperscalers, national AI initiatives, and research institutions across UAE, GCC, EMEA, and APAC regions procuring through Omnixon Global.

Ideal for

• Trillion-parameter large language model (LLM) training and fine-tuning requiring coherent, high-capacity unified memory across CPU and GPU
• High-throughput generative AI inference serving for enterprise deployments demanding low latency at massive scale
• Multi-physics scientific simulation and high-performance computing (HPC) workloads in energy, climate modeling, and life sciences
• Sovereign AI infrastructure buildouts for national AI initiatives requiring on-premises, air-gapped, high-density GPU compute
• Accelerated data analytics and recommendation system training at hyperscale datacenter density
• Drug discovery, genomics, and molecular dynamics workloads that benefit from large unified memory and high FP64 throughput

Technical specifications

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