Gigabyte G493-ZB2-AAE1 4U 8-GPU AMD EPYC 9754 AI Training Server

The Gigabyte G493-ZB2-AAE1 is a high-density 4U rack server engineered from the ground up for demanding AI training and deep learning inference workloads. Built around dual AMD EPYC 9754 processors from the Zen 4c-based Genoa family, the platform delivers up to 256 cores and 512 threads of computational bandwidth, paired with 12-channel DDR5 memory per socket for aggregate memory bandwidth exceeding 900 GB/s. The server accommodates up to eight full-height, double-width GPU accelerators in a topology designed to support NVLink bridging between adjacent cards, enabling tightly coupled multi-GPU communication essential for training large language models, multimodal transformers, and scientific simulation workloads at scale.

The G493-ZB2-AAE1 integrates a high-bandwidth PCIe 5.0 fabric connecting all eight GPU slots to the dual-socket EPYC platform, ensuring that CPU-to-GPU and GPU-to-GPU data paths do not become bottlenecks during gradient synchronization or activation checkpointing. The chassis supports OCP 3.0 NIC slots alongside dedicated 1GbE management ports, enabling high-speed RDMA-capable networking through InfiniBand HDR or 400GbE interconnects required in multi-node GPU cluster deployments. Onboard Aspeed AST2600 BMC provides out-of-band management via IPMI 2.0, Redfish, and Gigabyte's SMASH-compliant management suite.

Power delivery is handled by a 3200W (N+1) redundant PSU configuration with 80 PLUS Titanium-class efficiency, essential for sustaining continuous GPU TDP loads exceeding 2400W across all eight accelerators simultaneously. The thermal design incorporates high-static-pressure hot-swap fans organized in multiple zones with per-zone PWM control, maintaining GPU junction temperatures within specification under sustained training epochs in standard ASHRAE A2 datacenter environments. The platform is qualified for deployment in AI cloud infrastructure, national research computing facilities, and enterprise AI development clusters.

Ideal for

• Large language model pre-training and fine-tuning using frameworks such as PyTorch FSDP, Megatron-LM, or DeepSpeed with 8-way tensor and pipeline parallelism
• Multimodal AI model training combining vision transformers and language encoders requiring sustained all-to-all GPU communication via NVLink bridges
• High-throughput generative AI inference serving with batched request processing across all eight GPU accelerators for enterprise LLM API deployments
• Scientific simulation and HPC workloads including molecular dynamics, climate modeling, and computational fluid dynamics leveraging GPU-accelerated libraries on EPYC's high core count
• Federated learning node infrastructure within multi-rack AI training clusters interconnected via RDMA-capable networks for distributed gradient aggregation
• AI model compression, quantization-aware training, and neural architecture search workflows requiring iterative full-precision training runs with rapid checkpoint storage throughput

Technical specifications

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