NVIDIA H100 NVL 94GB HBM2e Dual-GPU Module

The NVIDIA H100 NVL 94GB HBM2e Dual-GPU Module, manufactured by Gigabyte under part number 900-21010-0010-000, is a high-density dual-GPU solution built on NVIDIA's Hopper architecture. The module integrates two H100 GPUs interconnected via NVLink, delivering a combined 94GB of HBM2e memory and exceptional aggregate memory bandwidth, making it one of the most capable accelerator platforms available for large-scale AI inference and training workloads. The Hopper architecture introduces the Transformer Engine with FP8 precision support, enabling dramatic throughput gains for large language models and generative AI applications compared to prior GPU generations.

Designed specifically for demanding datacenter environments, this dual-GPU module supports PCIe Gen 5 host connectivity and is engineered to fit into standard enterprise server chassis that accommodate the NVL form factor. The combined NVLink interconnect between the two H100 dies enables GPU-to-GPU communication at bandwidths far exceeding what PCIe alone can provide, which is critical for model-parallel and tensor-parallel inference workloads where data must move rapidly between GPU partitions. The module supports NVIDIA's full software stack, including CUDA, TensorRT, Triton Inference Server, and the NCCL communications library.

Targeted at enterprises operating AI factories, HPC clusters, and large-scale inferencing platforms across regions including the UAE, GCC, EMEA, and APAC, the H100 NVL 94GB module from Gigabyte provides a production-grade, datacenter-validated solution for organizations deploying frontier AI models, scientific simulation workloads, and high-throughput data analytics pipelines. Its high memory capacity and bandwidth make it particularly well suited to serving very large transformer-based models in a single module without model sharding across multiple nodes.

Ideal for

• Large language model (LLM) inference serving, enabling frontier models with tens to hundreds of billions of parameters to run within a single dual-GPU module at production throughput
• Distributed AI training for deep learning models requiring high inter-GPU bandwidth via NVLink to minimize communication overhead during gradient synchronization
• High-performance computing (HPC) simulations in fields such as computational fluid dynamics, molecular dynamics, and climate modeling that benefit from large on-device memory capacity
• Generative AI workloads including text, image, and multimodal model deployment in enterprise datacenter environments requiring low-latency, high-concurrency inference
• Data analytics and in-GPU-memory processing of very large datasets where the 94GB HBM2e capacity reduces the need for frequent data transfers from host memory
• AI-assisted drug discovery and genomics research requiring both large memory footprints and the high FP64 throughput characteristic of the Hopper architecture

Technical specifications

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