ASUS RS700Q-E12-RS4U 1U Quad-Node EPYC GPU Microserver

The ASUS RS700Q-E12-RS4U is a 1U rack-mounted quad-node microserver engineered around AMD EPYC 9004 series processors, delivering four independent compute nodes within a single rack unit. Each node operates as a self-contained server environment with its own CPU, memory subsystem, storage, and network connectivity, enabling high-density deployment strategies that dramatically reduce rack space consumption while preserving full per-node computational independence. The platform is built on ASUS's 12th-generation server architecture and targets organizations that require maximum compute density without sacrificing node-level configurability or isolation.

Each of the four nodes supports a single AMD EPYC 9004 series processor (Genoa), with per-node support for DDR5 registered ECC memory across multiple DIMM slots, enabling substantial memory capacity per node for memory-intensive workloads. The RS700Q-E12-RS4U integrates GPU support at the node level, allowing discrete GPU acceleration to be assigned per node for AI inference, machine learning training, and parallel scientific computing tasks. PCIe 5.0 connectivity ensures high-bandwidth communication between CPUs, GPUs, and NVMe storage devices, reducing bottlenecks in data-intensive pipeline workflows.

Designed for hyperscale data centers, cloud service providers, and enterprise AI infrastructure teams, the RS700Q-E12-RS4U supports NVMe storage per node and provides dedicated out-of-band management through ASUS ASMB11-iKVM baseboard management controllers, enabling remote monitoring, power control, and firmware updates for each node independently. The system is optimized for deployment in UAE, GCC, EMEA, and APAC enterprise environments where rack density, energy efficiency, and workload flexibility are primary operational requirements.

Ideal for

• High-density AI inference serving, where each of the four nodes independently hosts a GPU-accelerated inference engine to serve multiple model endpoints within a single rack unit
• Hyperscale cloud infrastructure buildout requiring maximum compute node count per rack while maintaining full node-level OS and workload isolation
• Edge data center deployments in space-constrained facilities across GCC and APAC regions where per-rack footprint directly impacts facility capacity
• Parallel scientific computing and simulation workloads that benefit from distributed multi-node execution across isolated EPYC Genoa compute environments
• Virtualization-dense environments running multiple VM clusters per node, leveraging EPYC's high core count and AMD SEV memory encryption for tenant isolation
• Distributed machine learning training pipelines that partition model or data parallelism across independent GPU-equipped nodes within a single chassis

Technical specifications

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