AIネットワーキングのためのテスト

スパイレント、業界初のイーサネット用AIワークロード エミュレーションを発表 AIファスト レーンの検証が可能に.AI特有のテストニーズと複雑性に対応する専用ソリューション.

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.

人工知能（AI）システムは、まだ発見されていない方法でデジタル産業を変革すると期待されている。ガートナーは、AIソフトウェアへの支出は2027年までに年平均成長率19.1%で2979億ドルに成長すると予測している。一方、ジェネレーティブAI（GenAI）ソフトウェアへの支出は、2023年のAIソフトウェアの8%から2027年には35%に増加する。 ジェネレーティブAIは縁の下の力持ちから最前線に躍り出た。この興奮の波の背後で、各業界はユースケースの証明、需要に応じたソリューションの拡張、AIの可能性を最大限に引き出すための革新に熱心に取り組んでいる。