How AI Compute Centers Structure Their Networks for Maximum Performance
This article explains the logical and physical architecture of AI compute centers, detailing the division into access, security, network, management, out‑of‑band, AI compute cluster, and general compute zones, and describes the four network planes—parameter, sample, business, and management—required for high‑performance AI workloads.
Artificial Intelligence (AI) compute centers provide training and inference compute, forming AI clusters via multiple cabinets interconnected.
The network is divided into zones: Access Zone (Internet and dedicated line access), Security Service Zone (DDoS, intrusion detection), Network Service Zone (vRouter, vLB, vFW), Management Zone (platform management systems and O&M components), Out‑of‑Band Management Zone (BMC and device‑management traffic), AI Compute Cluster Zone (servers integrating NPU, CPU, DPU with RDMA support), and General Compute Zone (resources for AI training and deep‑learning platforms).
The physical architecture optimizes the AI data‑center network into four planes: Parameter Plane (high‑bandwidth, lossless Ethernet for model‑parameter exchange, using CLOS, DragonFly+ or similar topologies), Sample Plane (high‑bandwidth, low‑latency storage access via RoCE), Business Plane (TCP/IP traffic for scheduling and management), and Out‑of‑Band Management Plane (device‑management traffic, typically gigabit links).
Key network design requirements include high throughput, reliability, intelligent operation, and support for RDMA‑enabled DPU cards to accelerate storage and ensure secure data transfer.
Additional technical articles and detailed diagrams are linked for further reading.
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