NVIDIA GB200 NVL2 Grace Blackwell Superchip (General Availability Ramp)

The NVIDIA GB200 NVL2 Grace Blackwell Superchip is a tightly integrated multi-chip module combining two NVIDIA Blackwell B200 GPU dies with a single NVIDIA Grace CPU via an ultra-high-bandwidth NVLink-C2C interconnect. The Grace CPU is an Arm Neoverse V2-based processor delivering 72 high-performance cores, while the two Blackwell GPU dies together present a unified 208 billion transistor compute engine. The NVLink-C2C chip-to-chip interconnect provides 900 GB/s of total bidirectional bandwidth between CPU and GPU, eliminating PCIe bottlenecks and enabling coherent memory access across a combined CPU and GPU HBM3e memory pool.

The GB200 NVL2 is engineered specifically for the most demanding generative AI training and inference workloads at scale. Each Blackwell GPU die incorporates a fifth-generation NVLink fabric interface, second-generation Transformer Engine with support for FP4 and FP8 precision, and a dedicated in-fabric all-reduce engine for accelerated collective communications. The aggregate HBM3e memory capacity across both GPU dies reaches 192 GB with 8 TB/s of total memory bandwidth, enabling large language models and multimodal foundation models to reside entirely in GPU-accessible high-bandwidth memory without host offloading.

Designed for dense rack-scale AI infrastructure, the GB200 NVL2 operates within the NVIDIA MGX mechanical specification and is the core building block of NVIDIA GB200 NVL72 rack-scale systems. It supports liquid cooling to manage the high thermal envelope associated with sustained AI compute throughput. Enterprise data centers deploying GB200 NVL2 gain access to NVIDIA's full software ecosystem including CUDA, cuDNN, TensorRT-LLM, and NeMo frameworks, making it suitable for sovereign AI deployments, large-scale model training clusters, and high-throughput inference services across UAE, GCC, EMEA, and APAC regions.

Ideal for

• Large language model pre-training and fine-tuning at scales exceeding hundreds of billions of parameters, leveraging FP4/FP8 Tensor Core throughput and high-capacity HBM3e memory
• High-throughput generative AI inference serving for enterprise-grade LLM and multimodal model deployments requiring low latency and maximum tokens-per-second output
• Sovereign AI and national AI infrastructure buildouts requiring dense, liquid-cooled rack-scale GPU clusters with coherent CPU-GPU memory access
• Scientific simulation and digital twin workloads combining CPU and GPU compute within a single coherent memory domain via the Grace Blackwell unified architecture
• Retrieval-augmented generation (RAG) pipelines and vector database acceleration where large in-memory indexes and rapid parallel query execution are critical
• Multi-node distributed deep learning research requiring high-bandwidth fifth-generation NVLink fabric for low-latency GPU-to-GPU collective communication across nodes

Technical specifications

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