Testing for AI Networking

Spirent Moves Testing into the AI Fast Lane with Industry’s First AI Traffic Emulation Platform for Ethernet

The scale of artificial intelligence (AI) applications is skyrocketing with the number of model parameters growing at a blistering 1000x pace every 3 years. The growing complexity and size of AI applications is driving a need for increasing numbers of AI accelerators, which in turn dictate the type and scale of the networking fabric required to interconnect the accelerators with each other and compute and memory resources. As AI applications move from small apps with hundreds of AI accelerators to large apps that require tens of thousands of AI accelerators, the networking architectures grow more complex. As we grow to over ten thousand accelerators, a data center scale architecture is required with a routable fabric and AI spine.

In today’s rapidly evolving technological landscape, Enterprise and datacenter networking has undergone significant advancements. With the widespread adoption of AI/ML, edge computing, and high-performance computing (HPC) systems, modern datacenters demand a network that delivers exceptional performance and minimal latency. This network must also seamlessly adapt to accommodate the tremendous surge in data transfer demands driven by data-intensive and compute-intensive applications.

AI/ML applications are driving new levels of high-speed Ethernet complexity for hyperscale data centers. Why emulation and validation tests are essential for ensuring new technologies perform as expected.

AI workloads are driving an unprecedented demand for low latency and high bandwidth connectivity between servers, storage, and GPUs.

The demand for data is growing exponentially and the datacenters need to keep pace. RoCEv2 has proven its usefulness over TCP/IP by providing the lowest latency by far in a legacy Ethernet datacenter.

Artificial intelligence (AI) systems are expected to transform digital industries in ways still being discovered. Gartner estimates spending on AI software will grow to $297.9B with a CAGR of 19.1% by 2027. While Generative AI (GenAI) software spend will rise from 8% of AI software in 2023 to 35% by 2027. Generative AI has moved from the fringes to the forefront, igniting imaginations about its vast potential. Behind this wave of excitement, industries are diligently working to prove use cases, scale solutions to meet demand, and innovate to unlock AI’s full potential.