دليل لبنية الذكاء الاصطناعي السيادية والبنية التحتية لوحدة معالجة الرسومات وعمليات النشر المختلطة

GPU cluster design. This involves deploying high-density servers, such as NVIDIA’s DGX or HGX platforms, each packed with multiple powerful GPUs (like the H100 or its successors) interconnected with high-bandwidth fabrics like NVLink. This tight integration allows for efficient distributed training, where a single model is trained across hundreds or even thousands of GPUs working in parallel.

High-performance storage system. Training large language models requires feeding the GPU cluster with vast amounts of data at extremely high speeds. This necessitates a parallel file system built on NVMe-based storage that can keep pace with the GPUs’ voracious appetite for data, preventing bottlenecks that would leave the expensive processors idle.

High-speed network. The GPUs within a cluster, and the clusters themselves, must be connected by a low-latency, high-bandwidth network. Technologies like InfiniBand or RoCE (RDMA over Converged Ethernet) are essential for the rapid communication required during distributed training, ensuring that the entire cluster operates as a single, cohesive supercomputer.

Data Center with Power and Cooling. GPU clusters generate immense heat and consume megawatts of power. This requires advanced cooling solutions and a resilient power grid, with a growing emphasis on sustainability, as seen in Siemens’ solar-powered, lake-cooled GPU infrastructure project.