GIGABYTE G492-Z53 4U GPU Server with 8x H100 SXM5

The GIGABYTE G492-Z53 is a 4U rack-mounted GPU server engineered around eight NVIDIA H100 SXM5 80GB GPUs interconnected via NVLink and NVSwitch fabric, delivering up to 3.2 exaFLOPS of FP8 AI inference throughput and 32 petaFLOPS of FP16 tensor performance across the full GPU complex. Built on the AMD EPYC 9004-series (Genoa) platform with dual CPU sockets, the system combines PCIe Gen 5 connectivity, DDR5 registered memory support, and a high-bandwidth SXM5 baseboard to ensure CPUs and GPUs exchange data at maximum throughput with minimal latency — a critical requirement for large language model training and high-performance scientific computing.

The G492-Z53 features a purpose-built thermal and power architecture rated to support the full 700 W TDP envelope of each H100 SXM5 module, with a redundant 80 PLUS Titanium-class power subsystem and high-airflow 4U chassis designed for continuous 24/7 datacenter operation. Storage flexibility is addressed through multiple NVMe U.2 bays and SATA/SAS backplane options, while dual high-speed network interfaces accommodate 400 GbE or InfiniBand HDR fabric connections that are standard in GPU cluster deployments. An out-of-band IPMI/BMC management interface with GIGABYTE Server Management (GSM) support enables remote monitoring, firmware updates, and power-capping without interrupting workloads.

Targeted at hyperscale operators, enterprise AI infrastructure teams, and HPC centers, the G492-Z53 (part number G492-Z53-AAX1) is validated for frameworks including NVIDIA CUDA, cuDNN, TensorRT, and NCCL, and is compatible with containerized environments such as NVIDIA NGC and leading Kubernetes-based orchestration stacks. Its dense 4U form factor maximises GPU density per rack unit while maintaining the serviceability, redundancy, and manageability standards expected in mission-critical enterprise datacenter environments across UAE, GCC, EMEA, and APAC regions.

Ideal for

• Large language model (LLM) pre-training and fine-tuning at scale using transformer architectures such as GPT and LLaMA variants
• High-throughput AI inference serving for generative AI applications requiring low-latency, multi-tenant GPU partitioning via MIG
• High-performance computing (HPC) simulations in computational fluid dynamics, molecular dynamics, and climate modelling
• Deep learning computer vision pipeline training for autonomous systems, medical imaging, and industrial quality inspection
• Multi-node GPU cluster compute nodes interconnected over InfiniBand or 400 GbE for distributed training at petaFLOP scale
• Sovereign AI infrastructure deployments within enterprise or government datacenters requiring on-premises GPU compute for data-residency compliance

Technical specifications

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