ASUS RS720Q-E12-RS12U Quad-Node 12-Bay GPU Server

The ASUS RS720Q-E12-RS12U is a 2U quad-node GPU server engineered for high-density enterprise computing, artificial intelligence inference and training, and large-scale data center deployments. Each of the four independent nodes is built on the Intel Xeon Scalable (Sapphire Rapids) platform, supporting dual processors per node and DDR5 registered ECC memory, delivering substantial aggregate compute capacity within a compact 2U rack footprint. The quad-node architecture enables operators to consolidate workloads that would otherwise require multiple discrete servers, reducing rack space consumption and simplifying infrastructure management.

Designed explicitly for GPU-accelerated workloads, the RS720Q-E12-RS12U provides PCIe 5.0 expansion across its nodes, enabling compatibility with current-generation accelerators used in deep learning model training, high-performance inference serving, and scientific simulation. The 12-bay storage configuration supports NVMe and SAS/SATA drives, providing the local storage bandwidth necessary for data-intensive AI pipelines and analytics workloads. Each node operates independently with its own baseboard management controller (BMC) supporting IPMI 2.0 and ASUS ASMB11 remote management, allowing granular out-of-band administration of individual compute nodes without interrupting adjacent nodes in the chassis.

The RS720Q-E12-RS12U is positioned for enterprises and hyperscale operators in sectors including cloud service provision, financial services HPC, government research, and telecommunications that require scalable, GPU-ready infrastructure with enterprise-grade reliability. Its integration with ASUS ASWM Enterprise management software and support for Redfish API enables seamless incorporation into modern software-defined data center environments. Omnixon Global supplies this platform to enterprise buyers across the UAE, GCC, EMEA, and APAC regions, supporting organizations building or expanding GPU compute infrastructure.

Ideal for

• Large-scale deep learning model training using multi-GPU configurations across independent quad-node compute resources
• High-throughput AI inference serving for real-time applications requiring low latency and high GPU density per rack unit
• High-performance computing (HPC) workloads in scientific research, genomics, and computational fluid dynamics
• Cloud service provider GPU-as-a-service offerings requiring dense, independently manageable multi-tenant compute nodes
• Financial services quantitative analytics and risk modeling demanding parallel compute acceleration
• Telecommunications and edge data center deployments requiring compact, power-efficient multi-node GPU infrastructure

Technical specifications

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