Gigabyte NVIDIA H200 SXM5 141GB HBM3e GPU Compute Module

The Gigabyte NVIDIA H200 SXM5 141GB HBM3e GPU Compute Module (GV-NH200SXM5-141G) is built on NVIDIA's Hopper architecture and represents the highest-memory configuration available in the H200 SXM5 form factor. Equipped with 141 GB of HBM3e high-bandwidth memory delivering approximately 4.8 TB/s of memory bandwidth, this compute module is engineered to handle the most memory-intensive AI training, large language model inference, and high-performance computing workloads at scale. The SXM5 socket interface enables NVLink 4.0 connectivity for multi-GPU configurations, allowing coherent high-bandwidth GPU-to-GPU communication across up to eight GPUs within a single NVLink domain.

The H200 SXM5 retains the same Hopper GPU die as its predecessor the H100 SXM5, while substantially expanding HBM capacity and memory bandwidth through the adoption of HBM3e memory stacks. This architectural continuity means existing Hopper-optimized software stacks, CUDA kernels, and frameworks including PyTorch, TensorFlow, and JAX require no modification to leverage the expanded memory envelope. The Transformer Engine embedded within the Hopper SM supports FP8, FP16, BF16, TF32, FP64, and INT8 precisions with hardware-accelerated mixed-precision execution, directly accelerating transformer-based neural network training and inference.

As a compute module in the SXM5 form factor, the GV-NH200SXM5-141G is designed for integration into validated server baseboards and GPU baseboard assemblies within high-density AI training clusters and inference servers. The module operates within NVIDIA's NVLink Switch System ecosystem, enabling NVLink-connected scale-up configurations. Enterprise datacenter deployments across AI research, scientific simulation, genomics, financial modeling, and large-scale inference benefit from the combination of 141 GB addressable GPU memory and the sustained compute throughput of the Hopper generation, making this module a foundational component for next-generation AI infrastructure across UAE, GCC, EMEA, and APAC datacenter environments.

Ideal for

• Large language model (LLM) training and fine-tuning for models exceeding 70 billion parameters, where expanded HBM3e capacity eliminates memory bottlenecks that would otherwise require model sharding across additional GPUs
• High-throughput generative AI inference serving, enabling larger batch sizes and longer context windows for production LLM deployments with reduced latency per token
• Computational fluid dynamics (CFD) and finite element analysis (FEA) simulations in engineering and scientific research requiring both high FP64 throughput and large in-GPU memory datasets
• Genomics and bioinformatics workloads including protein structure prediction and genome sequencing pipelines that benefit from retaining large reference datasets entirely within GPU memory
• Multi-GPU deep learning training clusters using NVLink 4.0 scale-up topologies for computer vision, recommendation system, and multimodal model development in enterprise AI research environments
• Financial risk modeling and quantitative analytics workloads requiring high-precision FP64 computation across large Monte Carlo simulation datasets within a single GPU memory space

Technical specifications

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