GIGABYTE XV23-ZX0 Liquid-Cooled GPU Server

The GIGABYTE XV23-ZX0 is a purpose-built direct liquid cooling (DLC) GPU node designed to address the extreme thermal and power density demands of large-scale AI inference and training clusters. The system integrates a closed-loop liquid cooling architecture that delivers coolant directly to GPU cold plates and CPU water blocks, eliminating the need for traditional air-cooling infrastructure and enabling sustained full-power operation across all accelerators simultaneously. This design philosophy dramatically reduces datacenter power usage effectiveness (PUE) by offloading heat rejection to facility-side cooling loops rather than relying on CRAC units and raised-floor airflow.

Architecturally, the XV23-ZX0 occupies a 2U chassis and is designed to support multiple high-end PCIe 5.0 GPUs in a NVLink or standard discrete configuration, paired with dual 4th or 5th Generation Intel Xeon Scalable or AMD EPYC processors on a server-grade motherboard platform. The node supports high-bandwidth DDR5 RDIMM memory across numerous DIMM slots, ensuring memory-to-compute ratios appropriate for large language model (LLM) serving, diffusion model inference, and distributed deep learning training runs. PCIe 5.0 connectivity between GPUs and CPUs minimizes data transfer bottlenecks, and the chassis accommodates high-throughput NVMe storage for model checkpointing and dataset staging.

The XV23-ZX0 is specifically optimized for deployment in hyperscale AI datacenters and enterprise GPU clusters where rack density, energy efficiency, and thermal reliability are primary operational concerns. The liquid cooling loop is compatible with industry-standard rear-door heat exchanger and in-row cooling manifold systems, making it deployable in both greenfield facilities and retrofitted colocation environments. GIGABYTE's server management ecosystem provides IPMI 2.0, Redfish API, and out-of-band management capabilities, enabling seamless integration with modern datacenter automation and orchestration platforms.

Ideal for

• Large-scale generative AI model training requiring sustained multi-GPU full-power operation without thermal throttling in dense rack configurations
• High-throughput LLM inference serving in hyperscale datacenter environments where per-rack GPU density directly impacts total cost of ownership at scale
• HPC simulation workloads in climate modeling, computational fluid dynamics, and molecular dynamics that demand continuous peak GPU utilization over extended periods
• Enterprise private AI cluster deployments in space-constrained facilities where liquid cooling enables higher compute density per square meter than air-cooled alternatives
• Multi-tenant GPU cloud infrastructure where predictable sustained performance and thermal isolation between workloads are critical service-level requirements
• Distributed deep learning research environments requiring tightly coupled high-bandwidth GPU interconnects with stable thermal operating conditions across multi-node training jobs

Technical specifications

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