Gigabyte G492-H80 8x GPU Server with H100/H200 SXM5 Tray (Bare)

The Gigabyte G492-H80 (part number G492-H80-AAX1) is a high-density 4U rack server engineered to support eight NVIDIA H100 or H200 SXM5 form-factor GPUs, delivered in a bare tray configuration that accommodates enterprise procurement workflows requiring GPU module flexibility. Built on a dual-socket Intel Xeon Scalable (Sapphire Rapids) platform, the system integrates NVIDIA NVLink and NVSwitch interconnect fabric to deliver fully unified GPU memory across all eight accelerators, enabling the extreme parallelism demanded by large-scale AI model training, high-performance inference, and scientific simulation workloads.

The G492-H80 is purpose-built for datacenter environments where computational density, thermal efficiency, and interconnect bandwidth are primary design constraints. The chassis incorporates a sophisticated direct liquid cooling architecture alongside high-volume airflow management to sustain continuous full-load GPU operation within standard datacenter thermal envelopes. Multiple PCIe Gen 5 expansion slots support high-bandwidth network adapters and NVMe storage controllers, ensuring the GPU compute fabric is not bottlenecked at the data ingestion or results egress layer.

As a bare tray variant, the G492-H80-AAX1 is oriented toward operators who source and install H100 or H200 SXM5 GPU modules independently, providing procurement and configuration flexibility within large-scale AI infrastructure buildouts. The platform is qualified for demanding enterprise and hyperscale datacenter environments across the UAE, GCC, EMEA, and APAC regions, and is available through Omnixon Global for organizations deploying frontier AI, HPC, and data-intensive workloads at scale.

Ideal for

• Large language model (LLM) pre-training and fine-tuning requiring multi-GPU tensor parallelism across eight SXM5 accelerators with high-bandwidth NVLink interconnect
• High-throughput AI inference serving for generative AI applications where low-latency, batched GPU execution at datacenter scale is required
• Computational fluid dynamics, molecular dynamics, and scientific HPC simulations that exploit unified GPU memory pools and FP64 double-precision throughput
• Deep learning recommendation model (DLRM) training for large-scale enterprise recommendation and personalization systems
• Multi-modal AI pipeline execution combining vision, language, and structured data models simultaneously across a shared high-bandwidth GPU fabric
• Sovereign AI and national research infrastructure deployments in the GCC and APAC regions requiring dense on-premises GPU compute with enterprise-grade reliability

Technical specifications

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