Huawei Network Equipment Cloudengine Xh9220 64dq Huawei 10g Sfp Switch 64x400ge Qsfp112

Huawei Network Equipment Cloudengine Xh9220 64dq Huawei 10g Sfp Switch 64x400ge Qsfp112

Product Overview

The CloudEngine XH9220-64DQ delivers 64×400GE QSFP112 ports, 51.2 Tbps switching capacity, NSLB network‑level load balancing, AI lossless network, and iFlashboot reliability – purpose‑built for HPC GPU clusters and AI training farms.

Key Differentiators (Bulleted for Buyer Focus)

• Ultra‑High Density 400GE – 64×400GE QSFP112 ports in 4U – ideal for spine‑leaf fabrics in AI clusters and intelligent computing centers.

• NSLB Network‑Level Load Balancing – Huawei proprietary algorithm dynamically balances traffic across all paths, eliminating congestion hotspots in GPU‑intensive training.

• AI Lossless Network – Hardware‑based PFC (Priority Flow Control) and ECN (Explicit Congestion Notification) ensure zero packet loss for RDMA/RoCE traffic – critical for distributed AI training.

• Massive Switching Capacity – 51.2 Tbps non‑blocking, 9,600 Mpps forwarding rate – handles 64 ports of full‑rate 400GE traffic simultaneously.

• iFlashboot Reliability – Boots from backup image automatically upon failure, reducing recovery time from hours to seconds.

• Optical Module Channel Resilience – Intelligent monitoring and fault isolation for optical modules, preventing single‑point failures from disrupting GPU clusters.

• Powerful Control Plane – 32‑core 2.2GHz CPU, 16GB RAM, 135MB packet buffer – ensures fast convergence and deep buffering for incast traffic patterns common in AI training.

• Redundant Power – 4×1200W hot‑swappable AC/DC power supplies (N+1 or N+N redundancy).

• 4U Compact Design – High port density per rack U, reducing spine switch count and inter‑rack cabling complexity.

Technical Specifications

Typical Deployments – AI training cluster spine/leaf fabric with 400GE GPU‑to‑GPU connectivity, intelligent computing center core aggregation, high‑frequency trading (HFT) low‑latency fabrics, hyperscale data center spine‑leaf architectures.