ASUS RS720Q-E11-RS8U GPU Node (A100 SXM4 80GB NVLink Config)

The ASUS RS720Q-E11-RS8U is a 2U quad-node GPU server platform engineered to deliver extreme parallel compute density for enterprise AI, HPC, and deep learning infrastructure. Each of the four independent nodes within the 2U chassis accommodates NVIDIA A100 SXM4 80GB GPUs interconnected via NVLink, enabling high-bandwidth GPU-to-GPU communication that dramatically accelerates multi-GPU workloads compared to standard PCIe topologies. The platform is built on the AMD EPYC 7003 series (Milan) processor architecture, pairing high core-count CPUs with the memory bandwidth and I/O capacity required to sustain throughput at the scale demanded by large model training and inference pipelines.

The A100 SXM4 80GB GPU at the heart of this configuration delivers 80GB of HBM2e memory per GPU with up to 2TB/s of memory bandwidth, alongside third-generation NVLink providing up to 600GB/s of bidirectional GPU interconnect bandwidth per node. The SXM4 form factor enables higher TDP envelopes than PCIe counterparts, translating directly into sustained compute performance for long-running training jobs. With support for the full NVIDIA AI software stack including CUDA, cuDNN, TensorRT, and NCCL, the RS720Q-E11-RS8U integrates cleanly into existing MLOps and HPC cluster environments.

Designed for large-scale datacenter deployment, the RS720Q-E11-RS8U supports high-speed networking fabrics including InfiniBand and 100GbE connectivity options to sustain inter-node communication at the bandwidth levels required by distributed training workloads. The chassis incorporates redundant power supplies and enterprise-grade thermal management suited to 24/7 continuous operation in colocation and private datacenter environments. ASUS system management tooling and IPMI/BMC support provide out-of-band management capability consistent with enterprise operational standards across UAE, GCC, EMEA, and APAC deployments.

Ideal for

• Large-scale deep learning model training, including transformer-based foundation models and generative AI architectures requiring high GPU memory capacity and NVLink interconnect bandwidth
• High-performance inference serving for latency-sensitive enterprise AI applications where 80GB HBM2e memory enables full large-model deployment without tensor parallelism overhead
• Computational fluid dynamics, molecular dynamics, and scientific simulation workloads that benefit from the A100 SXM4's FP64 double-precision compute throughput
• Multi-tenant GPU cluster deployments in enterprise private cloud or colocation environments where the quad-node 2U design maximises GPU density per rack unit
• Data analytics and in-memory GPU-accelerated processing pipelines using RAPIDS or similar frameworks operating on large datasets that require expanded GPU memory headroom
• Distributed HPC workloads using MPI over InfiniBand where tight CPU-GPU memory bandwidth and NVLink topology reduce communication bottlenecks across nodes

Technical specifications

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