NVIDIA HGX H200 8-GPU SXM5 Baseboard Module

The NVIDIA HGX H200 8-GPU SXM5 Baseboard Module (part number 920-23686-2540-000) represents the pinnacle of NVIDIA's Hopper architecture GPU platform, designed specifically for the most demanding AI training, large language model inference, and high-performance computing workloads in modern data centers. Built around eight NVIDIA H200 SXM5 GPUs interconnected via NVLink 4.0, the baseboard delivers a unified, tightly coupled compute fabric that enables massive parallel throughput across the full GPU cluster with minimal inter-GPU communication latency.

Each H200 GPU on this baseboard is equipped with 141 GB of HBM3e memory — a significant advancement over the H100's HBM3 configuration — delivering approximately 4.8 TB/s of aggregate memory bandwidth per GPU. This expanded memory capacity and bandwidth directly accelerates workloads that previously required multi-node distribution, allowing trillion-parameter models to be handled more efficiently within a single baseboard. The SXM5 form factor ensures full thermal and electrical integration with validated server platforms from brands.

The HGX H200 baseboard module is engineered for integration into purpose-built AI and HPC server platforms and operates as a drop-in acceleration solution within existing HGX-compatible infrastructure. With support for NVLink Switch System connectivity via NVSwitch 3.0 on the baseboard, all eight GPUs communicate at full NVLink 4.0 bandwidth, enabling high-throughput all-reduce operations critical to large-scale distributed AI training. The module is qualified for deployment across enterprise data centers, national AI research facilities, cloud infrastructure, and sovereign AI computing initiatives in regions including the UAE, GCC, EMEA, and APAC.

Ideal for

• Large-scale generative AI and large language model (LLM) training requiring high memory capacity and inter-GPU bandwidth, such as GPT-class and multimodal foundation models
• High-throughput LLM inference serving for enterprise AI applications where 141 GB HBM3e per GPU enables full model residence without offloading
• Scientific HPC simulation workloads including computational fluid dynamics, molecular dynamics, and climate modeling that benefit from FP64 Tensor Core performance
• Retrieval-augmented generation (RAG) and vector database acceleration for enterprise knowledge management and AI search platforms
• Sovereign AI and national data center deployments requiring maximum on-premises GPU compute density with full NVLink fabric integration
• Multi-tenant AI cloud infrastructure where GPU memory capacity per node directly increases concurrent model serving and utilization efficiency

Technical specifications

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