GIGABYTE MZ32-AR1 Motherboard-7003 Series Processors–E-ATX DP–280W

The GIGABYTE MZ32-AR1 is a dual-socket E-ATX server motherboard designed to support the AMD EPYC™ 7003 Series (Milan) processors. Built for HPC, virtualization, storage, and enterprise applications, it delivers outstanding scalability, high memory bandwidth, and PCIe 4.0 expandability. With a 280W CPU TDP envelope per socket, the MZ32-AR1 balances extreme multi-core performance with energy efficiency, making it a reliable platform for modern data centers and enterprise workloads.

Key Features

• Processor Support
  • Dual-socket AMD EPYC™ 7003 Series (Socket SP3) processors.
  • Up to 128 cores / 256 threads in a 2P configuration.
  • Optimized for multi-threaded and parallel compute environments.
• Form Factor
  • E-ATX (305 x 330mm) dual-processor design.
  • Rackmount-ready for cloud and enterprise servers.
• Memory
  • 8-channel DDR4 memory per CPU, delivering massive bandwidth.
  • Supports ECC DDR4 RDIMM/LRDIMM/3DS LRDIMM.
  • Capacity up to 4TB+ depending on memory modules.
  • ECC-enabled for mission-critical data integrity.
• Expansion Capability
  • Multiple PCIe Gen4 slots for GPUs, FPGAs, storage controllers, and high-speed NICs.
  • Ready for AI/ML, HPC acceleration, and virtualization scaling.
• Storage Options
  • Multiple SATA 6Gb/s ports for enterprise HDD/SSD arrays.
  • M.2 NVMe Gen4 slots for ultra-fast SSD storage.
  • RAID support for enterprise data redundancy.
• Networking
  • Integrated dual LAN ports (1GbE/10GbE options depending on SKU).
  • Expansion via PCIe NICs up to 25/40/100GbE for high-throughput networking.
• Power & Efficiency
  • Optimized for 280W CPU TDP per socket, ensuring high performance-per-watt.
  • Thermal design guarantees stable 24/7 operation.
• System Management
  • Integrated AST2500/AST2600 BMC with IPMI 2.0 & Redfish API.
  • Remote monitoring and control for CPU, memory, thermals, power, and storage health.

Applications

• High-Performance Computing (HPC) – For simulations, modeling, and large-scale research workloads.
• AI & Machine Learning – Supports accelerators for training, inference, and analytics.
• Cloud Computing & Virtualization – Ideal for SaaS, PaaS, and IaaS with dual-processor scalability.
• Enterprise Databases & Storage – Optimized for big data, analytics platforms, and storage nodes.
• Telecom & Edge Data Centers – Suitable for NFV, SDN, and distributed real-time compute environments.