Fujitsu PRIMERGY CX2550 M7 Server Node

The Fujitsu PRIMERGY CX2550 M7 is a single-node server blade engineered for deployment within the PRIMERGY CX2000 modular enclosure, delivering dense compute capacity for large-scale AI and high-performance computing environments. The system features dual 3rd-generation Intel Xeon Scalable processors with support for up to 24 NVIDIA H100/H200 GPUs or equivalent accelerators per 10U chassis through optimized slot configurations, enabling organizations to maximize GPU density and throughput in space-constrained data centers. The M7 generation incorporates enhanced PCIe Gen5 connectivity, advanced thermal management, and native support for distributed training frameworks, making it purpose-built for enterprises scaling GPU clusters for large language model training, computer vision inference, and scientific computing workflows.

Architecturally, the CX2550 M7 integrates dual-socket processor support with configurable memory up to 2.4TB per node, 10x 2.5-inch NVMe SSD bays for high-speed local storage, and multiple 25/100GbE network interfaces for low-latency inter-node communication. The blade design eliminates redundant power supplies and cooling infrastructure at the node level, delegating those functions to the shared CX2000 chassis, reducing operational overhead while maintaining reliability. Fujitsu's integrated management framework provides unified remote administration across all blades within the chassis, supporting heterogeneous GPU configurations and enabling dynamic workload distribution across multi-node deployments.

The system launched in Q1 2024 to address the rapid expansion of data-center operators building large-scale GPU compute clusters. Its blade-form-factor design, coupled with standardized thermal and power rails, significantly reduces deployment complexity compared to traditional rack-mounted systems while delivering enterprise-grade redundancy, predictable thermal behavior, and integrated cluster management capabilities.

Ideal for

• Large language model training and fine-tuning at scale, leveraging GPU density for reduced training time on multi-billion-parameter models
• Computer vision and deep learning research requiring high-throughput inference and experimentation across heterogeneous datasets
• Scientific computing and molecular dynamics simulations requiring sustained GPU compute for physics, chemistry, and materials research
• Financial modeling and risk analytics platforms executing complex Monte Carlo simulations and machine learning inference on structured market data
• Autonomous vehicle development pipelines processing and training on multi-modal sensor data (LiDAR, camera, radar) across distributed GPU clusters
• Enterprise data-center consolidation for on-premises AI/ML platforms, enabling scaling from pilot deployments to production multi-exaflop clusters

Technical specifications

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