HPE NVIDIA H200 SXM5 141GB GPU Computing Module

The HPE NVIDIA H200 SXM5 141GB GPU Computing Module (P65891-B21) is built on NVIDIA's Hopper architecture and delivers a major leap in high-bandwidth memory capacity over its predecessor, equipping enterprises with 141GB of HBM3e memory to handle the most memory-intensive AI and HPC workloads at scale. Engineered for integration into HPE's AI-optimized server platforms, this module connects via the SXM5 form factor, enabling the high-speed NVLink and NVSwitch interconnect fabric that sustains extreme bandwidth between GPUs in multi-GPU configurations.

At the core of the H200 is NVIDIA's fourth-generation Transformer Engine, which accelerates large language model (LLM) training and inference with support for FP8, FP16, BF16, TF32, and INT8 precision formats. The expanded 141GB HBM3e memory pool — paired with substantial memory bandwidth — directly addresses the bottleneck that limits serving very large foundation models and running long-context inference, making it a critical enabler for next-generation generative AI deployments. The module also retains full Hopper-generation capabilities including second-generation Multi-Instance GPU (MIG) technology for granular workload partitioning.

As an HPE-branded computing module, P65891-B21 is validated and integrated within HPE's ecosystem of AI infrastructure solutions, ensuring compatibility with HPE system management tools, firmware update pipelines, and data center thermal and power standards. This makes it well suited for enterprises in regulated industries or those requiring cohesive, vendor-validated AI infrastructure stacks across UAE, GCC, EMEA, and APAC regions.

Ideal for

• Training and fine-tuning large language models (LLMs) and multimodal foundation models requiring high memory capacity per GPU
• High-throughput generative AI inference serving for production deployments of models exceeding 70 billion parameters
• High-performance computing (HPC) simulations in life sciences, computational fluid dynamics, and climate modeling that demand large in-GPU memory footprints
• Enterprise AI data pipeline acceleration including embedding generation, vector search preprocessing, and retrieval-augmented generation (RAG) workloads
• Multi-tenant GPU partitioning via MIG for cloud-style shared AI infrastructure within private data centers
• Scientific research and national laboratory-scale workloads requiring FP64 double-precision compute alongside AI acceleration

Technical specifications

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