Jack Poller explores the revolutionary impact of Hammerspace on AI infrastructure, highlighting the potential for a single rack to manage up to one exabyte of data. This approach not only simplifies the physical footprint of data storage but also enhances the scalability and efficiency crucial for AI-driven enterprises. For more insights into AI Infrastructure Field Day 4, see the full coverage on our website.
Your GPUs Are Only as Good as the Network Feeding Them
In a recent article by Jack Poller about Cisco, he emphasizes the critical role of the network infrastructure in maximizing GPU performance for AI applications. He argues that irrespective of the GPUs’ computational capabilities, the overall system throughput and efficiency are heavily dependent on the robustness and speed of the underlying network. For additional insights and coverage from AI Infrastructure Field Day 4, visit Gestalt IT.
174: GreyBeards talk SDN chips with Ted Weatherford, VP Bus. Dev. & John Carney. Dist. Eng. at Xsight Labs
In the 174th episode of GreyBeards on Storage, hosts Ray Lucchesi and Jason Collier engage with Xsight Labs, to discuss the latest advancements in SDN (Software-Defined Networking) chips. The conversation illuminates the pivotal role these chips play in enhancing network performance and agility, essential for modern data center demands. For additional insights from AI Infrastructure Field Day 4, explore further coverage by Ray Lucchesi.
The Governance Controls Cisco Didn’t Know They Were Selling
Marian Newsome highlights the unexpected governance controls embedded in Cisco’s offerings, detailing how these features can significantly enhance enterprise IT security and compliance. She explores these controls within Cisco’s product line, noting their potential to inconspicuously support stronger governance strategies. For more insights on AI Infrastructure Field Day 4, check out continued coverage on LinkedIn Pulse.
Cisco’s Deterministic Ethernet: Closing the AI Networking Gap
Gina Rosenthal recently evaluated how Cisco is addressing the critical demands of artificial intelligence (AI) workloads with its Deterministic Ethernet solution, aimed at enhancing the reliability and precision of network traffic for AI applications. By minimizing latency and guaranteeing transmission time frames, this technology effectively closes the gap in networking for AI implementations, which is essential for the accuracy and efficiency of these systems. For more detailed coverage on this topic from AI Infrastructure Field Day 4, visit Gina Rosenthal’s contributions.
The Chip Inside Your Switch Matters More Than the Logo on the Front
In the world of networking hardware, the capabilities and performance largely depend on the chip technology within the switch rather than the branding on the exterior. Understanding the underlying silicon architecture can provide deeper insights into the switch’s efficiency, feature set, and overall value in an enterprise environment. For more insights like this article about XSight Labs, find additional coverage of AI Infrastructure Field Day 4 by Brad Gregory.
Cisco is Bringing Lossless AI Networking to the Enterprise
Cisco is advancing the enterprise networking landscape by integrating lossless AI networking capabilities, aiming to enhance data flow and reduce disruptions often associated with intensive AI computations. This change promises to optimize operations and ensure higher efficiency across networked AI applications. For more detailed coverage of AI Infrastructure Field Day 4, visit Gestalt IT.
Breaking the Data Gravity Curse in the AI Factory
In the latest discussion on breaking the data gravity curse in AI environments, Alastair Cooke highlights the challenges and solutions associated with managing large data sets that inhibit the mobility of applications across different platforms in an AI-driven ecosystem. Cooke explores how organizations are innovating to overcome these obstacles, enhancing efficiency and agility in their AI operations. For comprehensive insights on AI Infrastructure Field Day 4, visit Techstrong AI.
Cisco Reimagines AI Infrastructure for the Rest of Us
Alastair Cooke examines Cisco’s revamped approach to AI infrastructure, aiming to make it more accessible and efficient for broader user groups. He discusses how Cisco’s enhancements not only cater to large enterprises but also support smaller organizations striving to implement AI solutions. For a more comprehensive analysis of AI Infrastructure Field Day 4, visit Techstrong AI.
Ultra-Reliable Wireless for Your Physical and Mobile AI Devices
Alastair Cooke discusses the increasing reliance on ultra-reliable wireless networks to support physical and mobile AI devices, which are integral for maintaining seamless operations in various sectors. He explores the critical need for robust connectivity solutions that ensure these AI technologies function efficiently without interruption. For additional insights on AI Infrastructure Field Day 4, visit Techstrong IT.
What Solidigm Showed at AI Infrastructure Field Day 4
Gina Rosenthal provides a detailed overview of Solidigm’s presentation at AI Infrastructure Field Day 4, focusing on the latest advancements and implementations of their AI-driven storage solutions. She explains how these innovations are set to enhance data center efficiencies and optimize workload managements. For more insights on this topic, you can check out additional coverage of AI Infrastructure Field Day 4 by visiting our site.
Cisco Data Center Networking at AIIFD4
In his recent article, Peter J. Welcher discusses Cisco’s advancements and strategies in Data Center Networking, showcased at AI Infrastructure Field Day 4. He explores how Cisco is addressing modern data challenges through innovative infrastructure solutions aimed at enhancing performance and scalability. For more insights into AI Infrastructure Field Day 4, follow Peter’s updates on LinkedIn Pulse.
Quick Reaction: AIIFD4
Peter J. Welcher recently shared insights on the innovations and discussions from AI Infrastructure Field Day 4, highlighting key technology advancements and their implications for the industry. He emphasizes the practical application and impact of AI tools and infrastructure, providing a concise analysis to benefit professionals navigating these technologies. For more comprehensive coverage of AI Infrastructure Field Day 4, follow Peter J. Welcher’s updates on LinkedIn Pulse.
Cisco at AIIFD4: Fine-Tuning the Network for the AI Era
In his LinkedIn article, Ken Nalbone discusses Cisco’s presentation at AI Infrastructure Field Day 4, highlighting how Cisco is optimizing its networking solutions to better support AI-driven environments. The focus is on enhancing network architecture to efficiently manage the increased data and processing demands that come with AI applications. For more insights on AI Infrastructure Field Day 4, check out the coverage on LinkedIn Pulse.
How Unified Intelligence Shapes NetApp’s AI Vision
Gina Rosenthal provides an insightful analysis of how NetApp’s strategy for unified intelligence is sculpting its approach to artificial intelligence, emphasizing the integration of AI operations across different platforms. She highlights the role of cohesive data management and accessibility in enhancing AI outcomes. For more comprehensive coverage of AI Infrastructure Field Day 4, refer to Gestalt IT.
To Get Better Business Value, We Need Better AI Infrastructure
Alastair Cooke emphasizes the crucial role of robust AI infrastructure in harnessing maximum business value from AI technologies. He explores how advancements in infrastructure can directly enhance the performance and efficiency of AI applications, thereby impacting business outcomes positively. For additional insights into AI Infrastructure Field Day 4, explore further articles by Alastair Cooke on Techstrong AI.
AI Has Escaped Your Datacenter Presented by Cisco
AI has driven your datacenter designs and is now moving outwards through your whole network. This episode of the Tech Field Day podcast features Lee Peterson from Cisco discussing AI and networks with Andy Banta, Jack Poller, and Alastair Cooke. The discussion explores how AI is “escaping the data center” and becoming pervasive across the network, necessitating a dual focus on “networking for AI” and “AI for networking.” The former involves building robust, high-performance, and secure infrastructure, particularly at the edge, to support AI workloads like real-time inference. The goal is to support new applications such as robotics, fraud detection, and small language models, moving beyond traditional cloud-centric deployments to a more federated model. The latter leverages AI to manage, optimize, troubleshoot, and secure the network itself, with Cisco utilizing deep network learning models, historical data, and expertise to create AI assistants that enable intent-based networking and streamline operations. Additionally, the conversation emphasizes the critical role of advanced security, including hardware-accelerated post-quantum cryptography, to protect data in this evolving, AI-driven environment from future decryption threats.
Billion-Dollar AI Headlines Obscure Real Business Value
The big headlines that we’re seeing around the massive funding of large AI companies are a distraction from the reality that AI is being built and used in business applications. This episode of the Tech Field Day podcast features Frederic Van Haren, Chris Grundemann, Brian Martin, and Alastair Cooke reflecting after AI Infrastructure Field Day in Santa Clara. Popular news often covers the creation of large, general purpose AI models, yet the real-world application of AI through inference is where most companies see a return on their investment. Similarly, the common understanding of “AI” is as a single topic, without a more granular view that differentiates between rules-based systems, traditional machine learning, and emergent generative models like Large Language Models (LLMs). Specialized AI models will be vital for cost-effective applications with enhanced efficiency and the integration of diverse AI capabilities into agentic architectures. Advanced security protocols and regulatory frameworks are vital to mitigate novel vulnerabilities, organizations must adapt to an extraordinarily rapid pace of technological evolution. AI has already had a profound impact on software development, potentially enabling widespread custom application creation.
AI Needs Resource Efficiency
As we build out AI infrastructure and applications we need resource efficiency, continuously buying more horsepower cannot go on forever. This episode of the Tech Field Day podcast features Pete Welcher, Gina Rosenthal, Andy Banta, and Alastair Cooke hoping for a more efficient AI future. Large language models are trained using massive farms of GPUs and massive amounts of Internet data, so we expect to use large farms of GPUs and unstructured data to run those LLMs. Those large farms have led to scarcity of GPUs, and now RAM price increases that are impeding businesses building their own large AI infrastructure. Task-specific AIs, that use more efficient, task-specific models should be the future of Agentic AI and AI embedded in applications. More efficient and targeted AI may be the only way to get business value from the investment, especially in resource constrained edge environments. Does every AI problem need a twenty billion parameter model? More mature use of LLMs and AI will focus on reducing the cost of delivering inference to applications, your staff, and your customers.
Cutting-Edge AI Networking and Storage Kick Off 2026 at AI Infrastructure Field Day 4
We’re kicking off 2026 with one of our most popular events, AI Infrastructure Field Day 4, running from January 28th through January 30th. The event will stream live on LinkedIn, Techstrong TV, the Tech Field Day website, and for the first time ever, on our YouTube channel, offering a front-row view of the latest in […]