Supermicro 900-5G132-2220-110 NVIDIA HGX H100 80GB SXM5 Tray (Region Variant)

The Supermicro 900-5G132-2220-110 is an NVIDIA HGX H100 80GB SXM5 tray module engineered for the most demanding AI training, inference, and high-performance computing workloads in modern data centers. Built on NVIDIA's Hopper GPU architecture, this SXM5 form-factor board delivers 80GB of HBM3 memory per GPU with 3.35TB/s of memory bandwidth, enabling massive model parallelism and large-scale deep learning operations that are simply not achievable with prior-generation accelerators. The HGX H100 tray configuration integrates eight H100 SXM5 GPUs interconnected via NVLink 4.0, providing 900GB/s of GPU-to-GPU bandwidth across the NVLink fabric, dramatically accelerating multi-GPU workloads without CPU bottlenecks.

The Hopper architecture introduces the Transformer Engine with FP8 precision support, enabling up to 3,958 TFLOPS of FP8 tensor core performance per GPU. This is complemented by second-generation Multi-Instance GPU (MIG) technology, which allows each H100 to be partitioned into up to seven independent GPU instances, maximizing infrastructure utilization for mixed AI inference and HPC workloads. NVSwitch 3.0 fabric on the HGX tray provides full all-to-all GPU interconnect at 900GB/s aggregate NVLink bandwidth, making the tray an ideal building block for large-scale transformer model training at hundreds of billions of parameters.

The 900-5G132-2220-110 carries a region variant designation, indicating it is configured and validated for specific regional regulatory and certification requirements relevant to EMEA, GCC, and APAC markets. Supermicro's HGX H100 tray is designed to integrate directly into compatible Supermicro server platforms such as the SYS-821GE-TNHR and related HGX-class systems, providing a validated, production-ready path to deploying NVIDIA's flagship data center GPU architecture in enterprise environments. The tray-level form factor facilitates streamlined integration, serviceability, and scalability within hyperscale and enterprise AI infrastructure deployments.

Ideal for

• Large-scale generative AI and large language model (LLM) training, including models with hundreds of billions to trillions of parameters requiring high-bandwidth multi-GPU interconnect
• High-throughput AI inference serving for transformer-based models in production enterprise environments, leveraging MIG partitioning to serve multiple concurrent workloads
• High-performance computing (HPC) simulations in scientific research, climate modeling, and computational fluid dynamics benefiting from FP64 double-precision throughput
• Data center AI infrastructure build-out for cloud service providers and enterprise AI platforms requiring scalable, validated GPU tray modules
• Recommender system training and real-time personalization engines requiring massive memory capacity and bandwidth for embedding table operations
• Computer vision and multimodal AI model development requiring sustained tensor core throughput across mixed FP8, FP16, and BF16 precision workloads

Technical specifications

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