.png)
In the AI era,
Network is a core competitive advantage
AI data centers require networks with significantly higher bandwidth and lower latency than previous generations to support training of massive AI models and real-time inference. With the recent surge in data transmission demand driven by large-scale AI workloads, existing 100G, 200G, and 400G networks are reaching their limits, leading companies to upgrade to next-generation ultra-high-speed networks such as 800G Ethernet. Amidst this trend, Arista's RoCE (RDMA over Converged Ethernet) technology is attracting attention as a solution that dramatically improves network performance in AI data centers. Arita RoCE provides low latency and high throughput at the network layer, making it essential for building high-performance distributed training and real-time inference environments for massive AI models.
.png)
Problem
Why building and operating AI
network infrastructure is challenging.
Scalability and operational complexity.
-
As AI models continue to grow in size, challenges are emerging in scaling network infrastructure performance to support them.
-
Conventional data center networks are designed for general-purpose server-to-server traffic, whereas AI infrastructure requires ultra-high-speed connectivity among thousands of accelerator nodes.
-
Latency and bottlenecks during distributed training can lead to degradation of overall system performance.
-
Manually managing AI data center networks at the scale of thousands of nodes is impractical, making operational automation and real-time visibility critical challenges.
-
AI Ops and intent-based networking approaches are required to reduce operational workload and shorten issue resolution times.
Energy efficiency and cost pressure.
-
Network equipment vendors face challenges in reducing power consumption and costs.
-
Within the limited power and space budgets of AI data centers, supporting a large number of high-speed ports is required, making performance-per-watt improvements in switch chips and optical modules critical.
-
InfiniBand–dedicated interconnects deliver high performance but come with high costs for switches and NICs, as well as increased management complexity, creating barriers to enterprise adoption.
-
Achieving comparable performance on Ethernet-based networks requires advanced technologies, including congestion control, PFC-based lossless configurations, and sophisticated monitoring.
Service
Optimization services
DIA NEXUS focuses on.
AI workload–tailored design.
Guidelines for designing scalable, multi-tenant networks that interconnect hundreds or thousands of AI accelerators with high bandwidth, lossless performance, and low latency.
Intelligent network operations.
Proposing tools and platforms to improve the ease of operating and managing large-scale AI infrastructure.
Edge and cloud integration.
Providing network operation guidelines for building standard Ethernet–based edge networks and enabling seamless integration between edge and cloud environments.