ASRock Rack 2U4G-GENOA2/M3 GPU Server Barebone

The ASRock Rack 2U4G-GENOA2/M3 is a 2U rackmount GPU server barebone engineered around the dual AMD EPYC 9004 series (Genoa) processor platform, delivering a high-density compute foundation purpose-built for accelerated AI inference, training, and HPC workloads. The system accommodates up to four double-width full-height full-length (FHFL) PCIe 5.0 GPU accelerators, providing the PCIe bandwidth and thermal headroom required by modern data-center-class GPUs. Dual AMD EPYC Genoa CPUs bring up to 192 cores per system alongside support for DDR5 registered memory across a generous number of DIMM slots, ensuring that host-side memory bandwidth keeps pace with accelerator demand.

The barebone chassis integrates a purpose-designed server-grade backplane, redundant hot-swap power supply bays, and high-airflow cooling infrastructure sized for sustained full-GPU-load operation in 24/7 data center environments. Storage connectivity includes NVMe and SATA provisions for OS boot and local scratch storage, while the rear I/O panel supports out-of-band management through an IPMI-compliant baseboard management controller (BMC) for remote monitoring, power control, and firmware management without requiring in-band OS access.

Targeted at mid-density AI and HPC deployments where rack space is at a premium, the 2U4G-GENOA2/M3 strikes a balance between per-node GPU count and physical footprint, making it well suited for organizations building GPU clusters that require scalable, manageably dense nodes. As a barebone platform, it ships without CPUs, memory, storage, or GPUs, enabling procurement teams and system integrators to configure each node precisely to the requirements of their specific workloads and GPU technology choices.

Ideal for

• Large language model (LLM) training and fine-tuning clusters requiring dual-socket CPU headroom paired with up to four high-end GPU accelerators per 2U node
• AI inference serving deployments where mid-density GPU nodes are aggregated behind load balancers to handle high-throughput real-time inference requests
• Computational fluid dynamics (CFD) and finite element analysis (FEA) HPC workloads that leverage GPU acceleration alongside high core-count EPYC Genoa CPUs
• Genomics and molecular dynamics simulation environments requiring large per-node DDR5 memory capacity combined with GPU-accelerated processing
• GPU cluster buildouts for higher-education and national research institutions that need standardized, field-configurable barebone nodes across multiple grants and funding cycles
• Enterprise data science platforms running GPU-accelerated frameworks such as CUDA-based training pipelines where procurement of a validated, thermally characterized platform reduces integration risk

Technical specifications

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