Huawei Network Equipment 800ge 128 Port Manageable Huawei Sfp Switch Cloudengine Xh9330

Huawei Network Equipment 800ge 128 Port Manageable Huawei Sfp Switch Cloudengine Xh9330

Product Overview

The CloudEngine XH9330-128EO delivers 128×800GE ports, 102.4 Tbps switching capacity, Huawei self‑developed Solar ASIC, NSLB load balancing (>98% network throughput), and iFlashboot 2.0 (<5s restart) – purpose‑built for 64K‑node AI GPU clusters and intelligent computing centers.

Key Differentiators (Bulleted for Buyer Focus)

• Industry‑Leading 800GE Density – 128×800GE ports in 4U (optical‑electrical integrated, 800G/400G adaptive) – highest port count for AI spine‑leaf fabrics.

• Massive Switching Capacity – 102.4 Tbps non‑blocking, 76.8 Bpps forwarding rate – handles 128 ports of full‑rate 800GE traffic simultaneously.

• Huawei Solar ASIC – Self‑developed high‑performance chip optimized for AI traffic patterns, delivering lower latency and higher energy efficiency than commercial silicon.

• NSLB Network‑Level Load Balancing – Proprietary algorithm achieves >98% overall network throughput, eliminating congestion hotspots in distributed AI training.

• 64K‑Node AI Cluster Support – Scales to 64,000 × 800GE nodes – single fabric for massive GPU clusters without multi‑stage oversubscription.

• iFlashboot 2.0 – Sub‑5 second automatic restart upon failure, reducing AI training interruption to near zero.

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

• Flexible Cooling – Air‑cooled or liquid‑cooled optional – deploy in standard data centers or high‑density liquid‑cooled environments.

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

Technical Specifications

Typical Deployments – Large‑scale AI training cluster spine/leaf fabric with 800GE GPU‑to‑GPU connectivity, intelligent computing center core layer, hyperscale data center spine‑leaf architectures, 64K‑node AI networking for LLM (Large Language Model) training.