AI Infrastructure Field Day 2 is returning for a four day event you won't want to miss, from April 22–25 in Santa Clara, offering live insights into the latest AI infrastructure advancements. Starting with a full day at Google Cloud learning about Google’s AI hypercomputing systems, we'll be cont...

Mature technologies deliver business value by integration into boring production applications, so AI needs to be boring. This Tech Field Day Podcast episode features Max Mortillaro, Guy Currier, Jay Cuthrell, and Alastair Cooke. AI has frequently been in the public news, many organizations are bu...

As large language models scale, new challenges emerge - not only in maximizing GPU performance but also in validating the infrastructure that fuels the data pipeline used for training. On the front end, this includes securely ingesting user data from distributed cloud and customer environments in...

In this session, Crusoe shares how they are actively testing frontend networks and inter-VM/host data transfers that feed their GPU clusters. By validating the performance, reliability, and scalability of its infrastructure early, Crusoe aims to identify and resolve issues internally, minimizing ...

This session provides an overview of the Keysight AI (KAI) Data Center Builder solution and how it supports each phase of AI data center design and deployment with actionable data to improve performance and increase the reliability of AI clusters. The presentation explains how KAI Data Center Bui...

This session provides an overview of the Keysight AI fabric test methodology, demonstrating key findings and improvements achieved through automated testing and the search for optimal configuration parameters. Alex Bortek, Lead Product Manager at Keysight Technologies, introduces the Keysight AI ...

Sean Derrington, Product Manager, Storage at Google Cloud, introduced the AI Hypercomputer at AI Infrastructure Field Day, highlighting Google Cloud's investments in making it easier for customers to consume and run their AI workloads. The focus is on infrastructure with consideration to the cons...

Manjul Sahay, Group Product Manager at Google Cloud Storage, presented on Storage Intelligence with Google Cloud, focusing on helping customers, both enterprises and startups, manage their storage effectively for AI applications. These customers often face challenges in managing storage at scale ...

Ilias Katsardis, Senior Product Manager for AI infrastructure at Google Cloud, presented on the AI Hypercomputer Cluster Toolkit, addressing the complexities of deploying AI infrastructure on Google Cloud's compute engine and GKE. He highlighted the challenges customers face when trying to quickl...

Ishan Sharma, Group Product Manager in the Google Kubernetes Engine team, presented on GKE and AI Hypercomputer, focusing on industry-leading infrastructure, training quickly at mega scale, serving with lower cost and latency, economic access to GPUs and TPUs, and faster time to value. He emphasi...

Marco Abela, Product Manager at Google Cloud Storage, presented an overview of Google Cloud's storage solutions optimized for AI/ML workloads. The presentation addressed the critical role of storage in AI pipelines, emphasizing that an inadequate storage solution can significantly bottleneck GPU ...

Dan Eawaz, Senior Product Manager at Google Cloud, introduced Managed Lustre with Google Cloud, a fully managed parallel file system built on DDN Exascaler. The aim is to solve the demanding requirements of data preparation, model training, and inference in AI workloads. Managed Lustre provides h...

Michal Szymaniak, Principal Engineer at Google Cloud, presented on Rapid Storage, a new zonal storage product within the cloud storage portfolio, powered by Google's foundational distributed file system, Colossus. The goal in designing Rapid Storage was to create a storage system that offers the ...

Vivek Sarswat, Group Product Manager at Google Cloud Storage, presented on analytics storage and AI, focusing on data preparation and data lakes. He emphasized the close ties between analytics and AI workloads, highlighting key innovations built to address related challenges. The presentation dem...

Dennis Liu, a Product Manager at Google Cloud specializing in GPUs, presented on AI hypercomputer and GPU acceleration with Google Cloud. Liu covered Google Cloud's AI hypercomputer, from consumption models to purpose-built hardware. Focus was given to Google's cluster director for managing GPU f...

Rose Zhu, a Product Manager at Google Cloud TPU, presented on TPUs for large-scale training and inference, emphasizing the rapid growth of AI models and the corresponding demands for compute, memory, and networking. Zhu highlighted the specialization of Google's TPU chips and systems, purpose-bui...

This presentation by Aniruddha Agharkar, Product Manager at Google Cloud Networking, centers on Cloud WAN, Google's fully managed backbone solution designed for the enterprise era and powered by Google's planet-scale network. Customers have historically relied on bespoke networks using leased lin...

Vaibhav Katkade, a Product Manager at Google Cloud Networking, presented on infrastructure enhancements in cloud networking for secure, optimized AI/ML workloads. Focusing on the lifecycle of AI/ML, encompassing training, fine-tuning, and serving/inference, and the corresponding network imperativ...

Praful Lalchandani, VP of Product, Data Center Platforms and AI Solutions at Juniper Networks, opened the presentation by highlighting the rapid growth of the AI data center space and its unique challenges. He noted that Juniper Networks, with its 25 years of experience in networking and security...

Vikram Singh, Sr. Product Manager, AI Data Center Solutions at Juniper Networks, discussed maximizing AI cluster performance using Juniper's self-optimizing Ethernet fabric. As AI workloads scale, high GPU utilization and minimized congestion are critical to maximizing performance and ROI. Junipe...

AI clusters are high-value targets for cyber threats, requiring a defense-in-depth strategy to safeguard data, workloads, and infrastructure. Kedar Dhuru highlighted how Juniper's security portfolio provides end-to-end protection for AI clusters, including secure multitenant environments, without...

AI infrastructure is a critical but complex domain, and IT organizations face the pressure to deliver results quickly. Juniper Networks shows Juniper Apstra as a solution to streamline the management of AI data centers, providing proven designs. Kyle Baxter emphasizes the necessity of a robust ne...

Juniper Networks' presentation at AI Infrastructure Field Day focuses on designing AI data centers using Apstra, specifically emphasizing rail-optimized designs and highlighting Apstra's ability to create a fully functional network architecture in just minutes, incorporating native modeling for t...

This presentation by Kyle Baxter focuses on how Juniper Networks' Apstra solution can manage AI data centers at scale. Apstra simplifies network configuration for AI/ML workloads by providing tools to assign virtual networks across numerous ports, an essential capability in environments with pote...