SOLUTION 02

GPU Cluster Interconnect Solution

GPUDirect, RDMA, ultra-low latency network solutions for AI training clusters, HPC clusters, and distributed storage

Why GPU Clusters Need Dedicated Interconnect Networks

In modern AI training and HPC computing scenarios, the data exchange volume between GPUs far exceeds traditional general-purpose computing scenarios. A single training iteration of a trillion-parameter model may trigger PB-level gradient synchronization traffic. If network performance is insufficient, GPUs will spend long periods waiting for data, causing resource utilization to drop dramatically.

Core Interconnect Requirements

📈
Bandwidth Requirements

25G ~ 100G per link, meeting large-scale GPU cluster east-west traffic demands

Latency Requirements

End-to-end < 5μs (RDMA), avoiding GPU waiting that wastes computing power

🚀
Transmission Efficiency

Zero-copy, GPUDirect RDMA + SR-IOV, avoiding CPU intervention

Core Technology Overview

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RDMA Technology

RoCEv2 over UDP/IP, traversing Layer 3 networks, with PFC+ECN for lossless transmission, supporting GPUDirect RDMA features.

Latency < 5μs
🎮

GPUDirect RDMA

Driver-level support, NETI710 driver fully compatible with NVIDIA/AMD GPU drivers, zero-copy memory access.

Zero-Copy · Zero CPU Intervention
🖥️

SR-IOV Virtualization

Up to 64 VF/card, supporting KVM/vSphere mainstream virtualization platforms, flexible GPU resource partitioning.

64 VF/Card

🎯 Applicable Scenarios

  • LLM Training Gradient Synchronization
  • Distributed Inference Real-Time Scheduling
  • HPC Scientific Computing MPI Full Interconnect
  • Distributed Storage Large Block Sequential R/W

Core Requirements

  • High Bandwidth: 25G/100G Network Access
  • Ultra-Low Latency: End-to-End Microsecond Level
  • Zero Packet Loss: ROCEv2 Lossless Network
  • Scalable: Support 256+ Node Horizontal Scaling

Core Product Configuration

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NETI710-2CP

10G Dual Port SFP+

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NETI710-4CP

10G Quad Port SFP+

📡

25G SFP28

Optical Module

🔗

MPO High-Density

Rack Interconnect

Value Proposition

Unleash GPU Cluster Computing Potential

Through GPUDirect RDMA and ultra-low latency networks, eliminate GPU waiting time, improve cluster effective computing power utilization, and accelerate LLM training and scientific computing tasks.

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