In this conversation from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future,” Ayar Labs co-founder and CTO Vladimir Stojanovic joins theCUBE’s John Furrier to unpack how optical I/O is redefining AI-scale infrastructure. Stojanovic explains how Ayar Labs’ co-packaged optical engines (chiplets) attach to AI accelerators, switches and extended memory to deliver ultra-low latency, extreme bandwidth and strong energy efficiency, stitching together thousands of GPUs to operate as one system. He details why the next decade of AI will be won by interconnects: higher radix connectivity, flatter fabrics and fewer network hops to cut queuing delays and prevent expensive GPUs from idling. The discussion also surfaces notable metrics around rising rack densities (from ~30kW to 80kW, 120kW, 180kW – and talk of 600kW) and how in-package optics can help flatten power escalation by enabling many 100kW racks to deliver future-scale compute.
The interview dives into the product and ecosystem foundations enabling volume: Ayar Labs’ optical I/O chiplets in manufacturable form factors, partnerships with TSMC and Alchip to integrate optical engines alongside CoWoS interposers, memory and compute, and the backend test and assembly steps required for high-volume production. Stojanovic traces the journey from early DARPA-backed academic work (MIT, UC Berkeley, CU Boulder) and demonstrations with GlobalFoundries and IBM to today’s scale-up/scale-out convergence – spanning 1 → 8 → 32 → 64 → 256 → 512 → 1,000+ → 10,000 GPUs. He outlines why performance-per-TCO and interactivity are the key axes for AI factories, and shares a near-term execution window of roughly 18–24 months as Ayar Labs drives toward mass production.
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Justin Borgman, Starburst
In this conversation from theCUBE + NYSE Wired’s “AI Factories – Data Centers of the Future,” Ayar Labs co-founder and CTO Vladimir Stojanovic joins theCUBE’s John Furrier to unpack how optical I/O is redefining AI-scale infrastructure. Stojanovic explains how Ayar Labs’ co-packaged optical engines (chiplets) attach to AI accelerators, switches and extended memory to deliver ultra-low latency, extreme bandwidth and strong energy efficiency, stitching together thousands of GPUs to operate as one system. He details why the next decade of AI will be won by interconnects: higher radix connectivity, flatter fabrics and fewer network hops to cut queuing delays and prevent expensive GPUs from idling. The discussion also surfaces notable metrics around rising rack densities (from ~30kW to 80kW, 120kW, 180kW – and talk of 600kW) and how in-package optics can help flatten power escalation by enabling many 100kW racks to deliver future-scale compute.
The interview dives into the product and ecosystem foundations enabling volume: Ayar Labs’ optical I/O chiplets in manufacturable form factors, partnerships with TSMC and Alchip to integrate optical engines alongside CoWoS interposers, memory and compute, and the backend test and assembly steps required for high-volume production. Stojanovic traces the journey from early DARPA-backed academic work (MIT, UC Berkeley, CU Boulder) and demonstrations with GlobalFoundries and IBM to today’s scale-up/scale-out convergence – spanning 1 → 8 → 32 → 64 → 256 → 512 → 1,000+ → 10,000 GPUs. He outlines why performance-per-TCO and interactivity are the key axes for AI factories, and shares a near-term execution window of roughly 18–24 months as Ayar Labs drives toward mass production.
In this segment from theCUBE + NYSE Wired’s ‘AI Factories – Data Centers of the Future’, Starburst CEO Justin Borgman joins theCUBE Research’s Dave Vellante to unpack why data, not just power and silicon, is the essential fuel for AI factories. Borgman shares how Starburst’s federated approach helps enterprises assemble “data products” that carry business context and metadata to large language models, enabling useful, governed AI applications without forcing all data into a single store. The discussion highlights what Borgman is hearing this week at Starburst...Read more
