NVIDIA H100 NVL 94GB HBM2e Dual-GPU

The NVIDIA H100 NVL 94GB HBM2e Dual-GPU is a high-capacity accelerator module built on the NVIDIA Hopper architecture, engineered specifically for large-scale generative AI inference, training, and high-performance computing workloads in enterprise datacenter environments. Featuring dual H100 GPUs on a single NVLink board, the platform delivers a combined 94GB of HBM2e memory, enabling organizations to run exceptionally large language models and multi-modal AI workloads entirely within GPU memory without model partitioning overhead.

The H100 NVL leverages the fourth-generation NVLink interconnect to bind the two GPUs into a unified, high-bandwidth memory domain, providing up to 900 GB/s of GPU-to-GPU bandwidth. The Hopper architecture introduces the Transformer Engine, which dynamically applies FP8 and FP16 mixed-precision computation to dramatically accelerate transformer-based model inference while sustaining accuracy. Fourth-generation Tensor Cores further extend throughput across FP8, FP16, BF16, TF32, and INT8 precisions, making the platform well-suited across the full AI development lifecycle from exploratory research to production serving.

The 900-21010-0030-000 SKU is designed for PCIe-based server integration, offering broad compatibility with standard enterprise rack infrastructure without requiring proprietary NVLink Switch fabric chassis. This positions the H100 NVL as a practical choice for organizations scaling AI infrastructure incrementally, with each dual-GPU board delivering the combined compute density of two discrete H100 SXM5-class processors in a single PCIe form factor, supported by NVIDIA's mature software ecosystem including CUDA, cuDNN, TensorRT, and the full NGC catalog of enterprise-ready AI frameworks and containers.

Ideal for

• Large language model inference serving for enterprise generative AI applications requiring full model residency in GPU memory, including 70B+ parameter transformer models
• Distributed deep learning training for vision, language, and multimodal foundation models where high inter-GPU bandwidth reduces gradient synchronization overhead
• High-throughput scientific simulation and HPC workloads in computational chemistry, climate modeling, and genomics that benefit from large unified GPU memory capacity
• Enterprise AI platform consolidation, enabling multiple independent AI workloads to run concurrently via MIG (Multi-Instance GPU) partitioning across the dual-GPU module
• Retrieval-augmented generation (RAG) pipelines and vector database acceleration where simultaneous embedding, retrieval, and generation tasks demand sustained memory bandwidth
• Real-time AI inference at scale for financial risk modeling, fraud detection, and recommendation engines requiring low-latency, high-throughput GPU compute in standard PCIe datacenter servers

Technical specifications

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