HPE NVIDIA B200 SXM6 192GB GPU Computing Module

The HPE NVIDIA B200 SXM6 192GB GPU Computing Module (P68450-B21) is built on NVIDIA's Blackwell architecture, representing a significant generational leap in GPU compute density for enterprise data centers. The B200 SXM6 form factor connects via the high-bandwidth SXM6 socket interface, enabling NVLink 5 interconnects between GPUs within a system for dramatically higher multi-GPU aggregate bandwidth compared to prior generations. With 192GB of HBM3e memory per GPU, the module supports massive model sizes and large-scale dataset processing entirely in-memory, reducing the need for data offloading during inference and training operations.

The Blackwell architecture introduces a second-generation Transformer Engine with FP4 precision support alongside FP8, FP16, BF16, TF32, and FP64 compute modes, making it purpose-built for large language model training, fine-tuning, and high-throughput inference at scale. The B200 delivers substantially higher FP8 tensor TFLOPS relative to its predecessor H100, enabling enterprises to achieve greater model iteration velocity within existing power and rack-space envelopes. NVLink 5 provides high-speed peer-to-peer GPU communication within a node, supporting workloads that demand tightly coupled multi-GPU execution.

As an HPE-integrated module carrying part number P68450-B21, this GPU Computing Module is validated and supported within HPE's server and accelerated computing portfolio, ensuring compatibility with HPE's system management tooling, firmware update pathways, and enterprise support infrastructure. It is targeted at organizations deploying AI factories, high-performance computing clusters, and data center-scale inference platforms across industries including financial services, healthcare, government, and research.

Ideal for

• Large language model pre-training and fine-tuning at scale, leveraging 192GB HBM3e per GPU to hold multi-billion and trillion-parameter model shards in-memory
• High-throughput generative AI inference serving for enterprise applications requiring low-latency, high-concurrency responses from foundation models
• Scientific computing and simulation workloads in energy, climate modeling, drug discovery, and computational fluid dynamics that require FP64 double-precision accuracy at GPU scale
• Multimodal AI development including vision-language models, video understanding, and diffusion-based image and video generation at production scale
• Recommender system training for large-scale digital commerce and media platforms where embedding tables exceed the capacity of prior GPU memory generations
• Sovereign AI and national research infrastructure deployments requiring HPE-validated hardware integration with enterprise lifecycle management and compliance traceability

Technical specifications

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