AI Data Center Networking

Enterprise IT organizations are under pressure to “just do something” to take advantage of recent advancements in artificial intelligence (AI). Generative AI has quickly transitioned from R&D sideline to a

This is a joint blog authored by Kirk Compton (WW Director, Partnerships, SambaNova Systems) and Ben Baker (Sr. Director Data Center Marketing, Juniper Networks) Enterprises face mounting pressure to use

Data centers everywhere are under unprecedented stress—more traffic, new demands for digital transformation, and now AI applications coming online requiring innovative, exacting DC designs. Networks need to be more agile

In the first blog of this series, we covered the challenges of training LLMs. The previous blog reviewed the LLM consumption models for enterprises investing in AI (Makers, Takers, Shapers,

In the first blog of this series, we reviewed the complexity of developing foundational large language models (LLMs) and the vast resources required that place development outside the scope of

Training AI models is a special challenge. Developing foundational large language models (LLMs), like Llama 3.1 and GPT 4.0, requires massive budgets and resources reserved only for a handful of

Data center networks are an increasingly critical foundation of enterprise IT, but networking groups still tend to operate in silos, separate from the rest of the IT organization. Network administrators

Juniper Networks has been investing heavily to keep ahead of customer needs in the rapidly moving artificial intelligence (AI) market. In January 2024, we introduced the AI-Native Networking Platform, which uses the

In January 2024, Juniper introduced the AI-Native Networking Platform to leverage the right data via the right infrastructure to deliver the right responses for optimal user and operator experiences. Using