Tech Field Day Podcast

Are multi-terabyte GPUs and massive frontier models really the answer to enterprise AI, or are we hitting a hard wall of soaring memory costs and power constraints?
In this episode of the Tech Field Day Podcast, host Alastair Cooke sits down with technical experts Ron Pagani Jr., Andy Banta, and Brian Martin to examine whether brute-force hardware scaling remains sustainable.
The panel debates scale-up vs. scale-out architectures, emphasizing how smart software optimizations—such as persistent KV caching (MinIO MemKV, Solidigm) and intelligent data layers (CTERA)—can eliminate redundant GPU computation and lower costs.
They also explore agentic microservices, power and cooling bottlenecks, and why right-sizing AI models for production is critical for achieving real-world enterprise ROI.
Panelists:
Andy Banta, Consultant
Brian Martin, AI and Data Center Lead at Signal65
Ron Pagani Jr.⁠, Founder of Open Technology Partners
Hosts:
⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day
⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day
⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠
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As the explosive rise of AI workloads brings massive GPU clusters back to on-premises environments and surging neo-clouds, the race to maximize hardware utilization has made multi-tenancy an absolute necessity.
In this episode of the Tech Field Day Spotlight Podcast, host Tom Hollingsworth sits down with networking experts Scott Robohn, Ed Weadon, and Netris CEO and Cofounder Alex Saroyan to dissect the hidden complexities of modern AI infrastructure. Managing thousands of GPUs means navigating dense networking architectures like quad-planes, massive power restrictions, and the intricate blending of Ethernet and InfiniBand environments.
Alex reveals how Netris solves these scaling bottlenecks from Day 0 using digital twin simulations and automated network abstraction, allowing operators to dynamically isolate cluster resources for different tenants through simple, unified API calls.
Netris Representative:
Alex Saroyan, Cofounder and CEO of Netris
Scott Robohn, CEO of Solutional
Ed Weadon, Network Engineer
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Imagine walking into the office, grabbing your morning coffee, and realizing your network dashboard is perfectly green because an AI agent silently resolved every issue while you slept.
In this episode of the Tech Field Day Podcast, Imagine walking into the office, grabbing your morning coffee, and realizing your network dashboard is perfectly green because an AI agent silently resolved every issue while you slept.
In this Spotlight episode of the Tech Field Day Podcast presented by HPE Networking, host Tom Hollingsworth sits down with network practitioners Keith Parsons and Mark Houtz, alongside Hewlett Packard Enterprise’s Sunalini Sankhavaram, to explore the paradigm shift from reactive, AI-driven troubleshooting to proactive, self-driving networks.
They discuss the critical issue of trust in automation, detailing how HPE's Marvis progresses from "driver-assist" recommendations to fully autonomous remediation. From preventing broadcast storms in hospitals to lightening the load for budget-constrained IT teams, discover how agentic AI and digital twins are reshaping the future of enterprise operations without taking away the essential human in the loop.
This and more on the Tech Field Day Podcast Spotlight presented by HPE Networking.
Try Mist AI: https://www.hpe.com/us/en/mist-ai.html
HPE Networking: http://hpe.com/networking
HPE Representative:
Sunalini Sankhavaram, VP of Product Management at HPE
Keith Parsons, Network Engineer
Mark Houtz, Network Engineer
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Enterprises are increasingly turning to open networking and they need guidance to ensure the right choices are being made. A great partner can ease the transition.
In this episode of the Tech Field Day podcast, brought to you by Cisco, Tom Hollingsworth is joined by Scott Robon, Ryan Booth, and Cisco’s Surbhi Paul explore how AI infrastructure demands are pushing organizations to rethink their network architectures.
The consensus is that while SONiC was once primarily a hyperscaler and experimental technology, it has matured significantly and is now appearing in real enterprise and U.S. government deployments. Cisco’s position is to meet customers where they are, supporting both their established NX-OS/ACI platforms and Sonic on Nexus 9K hardware, giving organizations the flexibility to shift software strategies without replacing physical infrastructure.
Open networking isn’t a one-size-fits-all solution. It requires honest assessment of a team’s internal skill set and operational maturity. Cisco frames its SONiC offering around three pillars: the Cisco 8000 series for hyperscale and web-scale use cases, the Nexus 9K for enterprise AI fabrics, and Nexus Hyperfabric for organizations that want the benefits of SONiC without the complexity of managing it themselves. Cisco differentiates itself by backing Sonic deployments with 24/7 TAC support and lifecycle management tools, bridging the gap between open-source flexibility and the reliability enterprises depend on.
Panelists:
Cisco Representative: Surbhi Paul, Product Leader at Cisco
Scott Robohn, CEO of Solutional
Ryan Booth, AI Consultant and Architect
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Learn more about AI Infrastructure Field Day 5 here.
Think you can just buy more GPUs to solve your AI infrastructure problems? Think again.
In this episode of the Tech Field Day Podcast, recorded live on-site at AI Infrastructure Field Day 5, host Alastair Cooke sits down with industry experts Ray Lucchesi, Frederic Van Haren, and Brandon Whichard to discuss why the endless race for new hardware is hitting a wall.
With supply chain shortages and massive power demands making upgrades difficult, companies must shift their focus toward optimizing the technology they already own. The panel explores how clever software tools, smaller foundation models, and efficient token management can squeeze maximum performance out of existing data centers.
The panel explores the real secret to scaling AI lies in smart software practices rather than just buying more chips.