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
25G ~ 100G per link, meeting large-scale GPU cluster east-west traffic demands
End-to-end < 5μs (RDMA), avoiding GPU waiting that wastes computing power
Zero-copy, GPUDirect RDMA + SR-IOV, avoiding CPU intervention
Core Technology Overview
RDMA Technology
RoCEv2 over UDP/IP, traversing Layer 3 networks, with PFC+ECN for lossless transmission, supporting GPUDirect RDMA features.
GPUDirect RDMA
Driver-level support, NETI710 driver fully compatible with NVIDIA/AMD GPU drivers, zero-copy memory access.
SR-IOV Virtualization
Up to 64 VF/card, supporting KVM/vSphere mainstream virtualization platforms, flexible GPU resource partitioning.
🎯 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
NETI710-2CP
10G Dual Port SFP+
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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