Leaders in robotics and artificial intelligence infrastructure discuss
device-scale foundation models, architecture alternatives to transformers and
enterprise deployment challenges. The conversation addresses model efficiency,
edge inference, hybrid on-device and cloud routing, and in-car multimodal
intelligence use cases to inform enterprise strategies around cost, data
sovereignty and privacy. Ramin Hasani of Liquid AI is co-founder and chief
executive officer and a leader in developing Liquid Foundation Models for
device-scale deployment. Hasani explains model architecture choices,
memory-optimized deployment and edge inference approaches. They highlight how
smaller Liquid Foundation Models combined with fine-tuning enable specialized
vertical solutions while reducing memory and compute footprints. Analysts on
theCUBE note transformer limits and key-value cache scaling drive exploration of
new architectures. They identify continuous learning, edge networking and
production data flywheels as central elements for enterprise deployment. The
discussion provides practical considerations for deploying foundation models at
device scale, including cost management, model efficiency, privacy and
regulatory compliance. Hosts John Furrier and Dave Vellante with analyst Howie
Xu conduct the theCUBE Research interview to surface implications for robotics
and AI infrastructure and deployment strategies.
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In this interview from theCUBE + NYSE Wired: Robotics & AI Infra Leaders 2026, Ramin Hasani, co-founder and chief executive officer of Liquid AI, joins theCUBE's John Furrier and Howie Xu to discuss how specialized, efficient AI models are powering intelligence at the edge. Hasani explains how Liquid Foundation Models bring specialized intelligence outside the data center, running on devices as small as a Raspberry Pi. He details a new production deal with Mercedes-Benz, where a 600-megabyte multimodal model powers in-car voice intelligence on low-cost automo...Read more