Gigabyte G293-Z43-AAE1 4U GPU Server AMD EPYC 9004

The Gigabyte G293-Z43-AAE1 is a purpose-built 4U rack server engineered around AMD's EPYC 9004 series processors, offering a dense and high-bandwidth platform for accelerated computing workloads. The system leverages the AMD EPYC 9004 architecture with support for up to 128 cores, delivering exceptional parallel compute throughput paired with a high-capacity PCIe 5.0 fabric that feeds up to eight double-width GPU accelerators simultaneously. With memory bandwidth reaching up to 4800 MT/s across 12 DDR5 DIMM slots and support for high-speed NVMe storage, the server eliminates common bottlenecks that limit GPU utilization in deep learning pipelines.

Designed for large-scale AI training clusters and inference farms, the G293-Z43-AAE1 accommodates enterprise-class GPUs such as NVIDIA H100 and AMD Instinct MI300X through its eight full-height, full-length PCIe 5.0 x16 slots. The chassis integrates a high-efficiency power subsystem with redundant 3000W or higher platinum-rated PSUs to sustain continuous full-GPU-load operations. Advanced thermal management through optimized airflow channels and high-static-pressure fans ensures stable performance under sustained compute loads typical of multi-day model training runs.

The platform supports enterprise manageability through an integrated Gigabyte Management Controller (BMC) with IPMI 2.0, Redfish API, and out-of-band management, enabling seamless integration into modern data center orchestration frameworks including Kubernetes and Slurm-based HPC schedulers. Its 4U form factor balances GPU density with adequate cooling headroom, making it a practical building block for GPU clusters requiring both computational density and operational reliability in co-location and hyperscale data center environments.

Ideal for

• Large-scale generative AI model training using transformer architectures such as LLMs and diffusion models requiring multi-GPU data parallelism
• High-throughput deep learning inference serving for production AI applications demanding low-latency GPU accelerator access
• HPC simulation workloads including computational fluid dynamics, molecular dynamics, and climate modeling benefiting from EPYC's high core count and memory bandwidth
• Multi-tenant GPU cloud infrastructure where dense GPU-per-rack-unit ratios reduce infrastructure overhead costs per accelerated compute node
• Computer vision and autonomous systems development requiring continuous batch training across large labeled datasets with high I/O throughput
• AI research clusters in academic and enterprise environments running distributed training frameworks such as PyTorch DDP, Horovod, and DeepSpeed across multiple nodes

Technical specifications

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