ASUS RS700Q-E12-RS8U Quad-Node GPU Server

The ASUS RS700Q-E12-RS8U is a 1U quad-node GPU server engineered for high-density artificial intelligence, machine learning, and HPC deployments. Each of the four independent nodes is built on the Intel Xeon Scalable (Sapphire Rapids) platform and supports PCIe 5.0 connectivity, enabling high-bandwidth communication between CPUs, GPUs, and NVMe storage. The architecture consolidates four discrete compute nodes into a single 1U chassis, dramatically improving rack-space efficiency without sacrificing per-node computational headroom.

Every node accommodates dual Intel Xeon Scalable processors and supports DDR5 registered ECC memory across multiple DIMM slots, providing the memory bandwidth necessary for large-scale AI model training and inference. Each node connects to dedicated GPU resources and high-speed NVMe storage through PCIe 5.0 lanes, while out-of-band management is handled via ASUS ASMB11 baseboard management controllers supporting IPMI 2.0 and ASUS Control Center. The system is designed for 24/7 continuous operation in enterprise datacenters and colocation facilities.

The RS700Q-E12-RS8U addresses the requirements of organizations deploying GPU-accelerated workloads at scale, including generative AI model serving, large language model training, scientific simulation, and real-time data analytics. Its quad-node design reduces per-node total infrastructure overhead while the ASUS enterprise server ecosystem ensures compatibility with standard datacenter power, cooling, and management frameworks across UAE, GCC, EMEA, and APAC deployments.

Ideal for

• Large language model (LLM) training and fine-tuning requiring dense GPU compute across multiple independent nodes within a single rack unit
• High-throughput AI inference serving for enterprise applications including computer vision, natural language processing, and recommendation engines
• High-performance computing (HPC) workloads such as computational fluid dynamics, molecular dynamics simulation, and finite element analysis
• Hyperscale cloud and colocation datacenter deployments demanding maximum compute density per rack unit to minimize power and space overhead
• Real-time data analytics and deep learning pipelines processing large-scale structured and unstructured datasets at the edge or in core datacenters
• Multi-tenant AI platform deployments where each independent compute node can be allocated to separate teams, projects, or virtualized environments

Technical specifications

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