Gigabyte NVIDIA A100 SXM4 80GB GPU Module

The Gigabyte NVIDIA A100 SXM4 80GB GPU Module (MPN: 900-21020-0020-000) is a high-performance compute accelerator built on NVIDIA's Ampere architecture, designed to meet the demanding requirements of modern AI training, high-performance computing (HPC), and large-scale data analytics workloads. Utilizing the SXM4 form factor, it connects via NVIDIA's NVLink and NVSwitch interconnect fabric, enabling multi-GPU configurations with substantially higher bandwidth than PCIe-based alternatives. The 80GB HBM2e memory subsystem delivers 2TB/s of memory bandwidth, making it well-suited for memory-intensive models and datasets that cannot be accommodated by lower-capacity accelerators.

The A100 SXM4 80GB is built around 6,912 CUDA cores and 432 third-generation Tensor Cores, which accelerate mixed-precision matrix operations central to deep learning frameworks including TensorFlow, PyTorch, and MXNet. The module supports TF32, FP64, FP32, FP16, BF16, and INT8 precisions, providing flexibility across scientific simulation, inference optimization, and training pipelines. Multi-Instance GPU (MIG) technology allows the physical GPU to be partitioned into as many as seven independent GPU instances, each with dedicated memory, cache, and compute resources, enabling secure and efficient resource sharing across multiple concurrent workloads or tenants.

As an SXM4 form factor module, this unit is intended for installation into compatible server platforms such as the NVIDIA HGX A100 baseboard or qualified server systems that support the SXM4 socket. It interfaces with NVLink 3.0 for up to 600GB/s bidirectional GPU-to-GPU bandwidth in multi-GPU topologies. The module is a foundational building block for AI supercomputing clusters, large language model (LLM) training infrastructure, genome sequencing pipelines, and financial risk simulation environments deployed across enterprise datacenters, cloud service providers, and research institutions.

Ideal for

• Large-scale deep learning model training including transformer-based LLMs and computer vision models requiring high memory capacity and sustained FP16/BF16 throughput
• High-performance computing simulations in computational fluid dynamics, molecular dynamics, and climate modeling leveraging FP64 double-precision performance
• Multi-tenant GPU virtualization via MIG, allowing enterprise datacenters to partition a single accelerator into isolated instances for concurrent workloads across teams or departments
• AI inference at scale for real-time and batch inference pipelines requiring low-latency INT8 or FP16 execution on large neural network models
• Genomics and life sciences workloads including DNA sequencing acceleration, protein structure prediction, and drug discovery simulation
• Financial services quantitative computing including Monte Carlo simulations, risk analysis, and algorithmic model backtesting requiring high memory bandwidth and FP64 precision

Technical specifications

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