GIGABYTE R163-Z36 1U EPYC 9005 Server

The Gigabyte R163-Z36 is a 1U rack server built around AMD's fifth-generation EPYC 9005 series processors (codenamed Turin), delivering up to 192 cores per socket in a compact single-socket form factor. Leveraging the Zen 5 microarchitecture and AMD's advanced 3D chiplet design, the platform supports up to 12 DDR5 RDIMM/LRDIMM slots across six memory channels, enabling substantial memory bandwidth for latency-sensitive and throughput-intensive workloads. The server targets hyperscale operators, colocation providers, and enterprise IT teams that need to maximize compute density per rack unit while keeping per-node infrastructure overhead minimal.

The R163-Z36 is optimized for scale-out cloud-native environments, supporting NVMe and SATA storage configurations in a tray-based 1U chassis with hot-swap drive bays. The platform integrates a dedicated IPMI-based BMC (Gigabyte's MegaRAC SP-X) for out-of-band management, enabling remote provisioning, power monitoring, and lifecycle management at scale. PCIe 5.0 expansion lanes from the EPYC 9005 SoC provide direct attach capability for high-bandwidth accelerators, SmartNICs, and NVMe drives without the latency overhead of a discrete PCH.

As a purpose-built node for horizontally scaled architectures, the R163-Z36 is well suited for containerized microservices, CDN edge caching, distributed storage backends, HPC scheduler nodes, and AI inference frontends where raw core density, memory capacity, and I/O throughput must coexist within strict power and space budgets. Dual redundant hot-swap power supplies and tool-less component access further reduce operational friction in large fleet deployments.

Ideal for

• Hyperscale cloud compute nodes: deploying dense pools of vCPUs for virtualized tenant workloads in OpenStack or VMware environments requiring high core-per-U ratios
• Kubernetes and container orchestration: running large numbers of containerized microservices with Zen 5 core efficiency reducing per-pod infrastructure overhead
• AI and ML inference serving: hosting quantized LLM inference engines or real-time scoring services where high core counts handle concurrent request parallelism
• CDN and edge caching: deploying stateless, scale-out edge nodes that stream cached media and API responses with minimal latency across distributed PoPs
• Distributed storage metadata services: acting as Ceph MDS, etcd, or Kafka broker nodes where CPU headroom and fast NVMe I/O are equally critical
• HPC job scheduling and MPI ranks: serving as dense compute nodes in Slurm or PBS clusters running CFD, genomics, or finite-element analysis workloads

Technical specifications

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