AI Computing Center Network Solution
High-bandwidth, low-latency, lossless network solutions for large AI data centers and AIDC clusters, supporting trillion-parameter LLM training
Why AI Computing Center Networks Matter
With the explosive growth of LLMs, AI training and inference workloads, AI Computing Centers (AIDC) have become the core of new digital infrastructure. Trillion-parameter model training requires thousands or even tens of thousands of GPUs working together, with unprecedented east-west traffic volume, demanding extremely high requirements from the underlying network infrastructure.
Key Customer Pain Points
Traditional 1G/10G NICs cannot meet AI training demands, resulting in low GPU utilization
Switch congestion causes RDMA performance degradation of 30%~50%
Mixed vendor environments make fault diagnosis difficult, MTTR is high
Three-Tier Network Architecture Design
Server Access
- EZMAX NETI710 Series NICs
- 10G/25G/100G Multi-Spec
- SR-IOV Virtualization Support
- GPUDirect RDMA
Rack Interconnect
- 25G/100G Optical Modules
- MPO High-Density Cabling
- RoCEv2 Lossless Configuration
- VXLAN/EVPN Support
Core Backbone
- 100G/400G Backbone Interconnect
- Full Mesh or Fat-Tree Topology
- ECMP Multi-Path Load Balancing
- Cross-POD/Cluster Expansion
🎯 Applicable Scenarios
- Large AI Computing Center Construction
- AIDC Artificial Intelligence Data Centers
- Cloud Computing Infrastructure
- Large-Scale ML Training Clusters
⚡ Core Requirements
- High Bandwidth: 100G/400G Network Architecture
- Low Latency: End-to-End Microsecond Latency
- Lossless Network: ROCEv2 Storage Network
- High Density: High-Density Rack Deployment
Core Product Configuration
NETI710-2CP
10G Dual Port SFP+
NETI710-4CP
10G Quad Port SFP+
25G SFP28
100m/10km
100G QSFP28
100m/10km
Value Proposition
Supporting Trillion-Parameter LLM Training
Through high-performance 100G/400G network architecture, optimize east-west traffic, reduce communication overhead, accelerate GPU cluster collaboration efficiency, and achieve efficient training of trillion-parameter models.
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