Darren Chien of Positron AI, managing director for Asia Pacific, APAC, discusses Positron's field-programmable gate array, FPGA-based Atlas product and its upcoming Asimov chip and Titan system within the context of robotics and artificial intelligence, AI infrastructure. Chien explains that inference is fundamentally an economics and energy-efficiency problem and they emphasize optimization for tokens per dollar and tokens per watt. They highlight the distinction between compute-bound prefill and memory-bound decode, the importance of context windows and Titan's air-cooled design that enables legacy enterprise deployment without costly retrofits. The conversation addresses memory capacity and memory bandwidth as critical factors for memory-optimized systems and for enterprise scaling across Taiwan, Korea, Japan and Singapore. This theCUBE Research conversation with hosts John Furrier and Gabe Olave occurs at the NYSE Wired AI Leaders Summit. The hosts discuss market competition and open models and underscore APAC's strategic role in silicon and supply chains.
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Darren Chien, Positron AI
July 28, 2026
Darren Chien
Managing Director, APAC Positron AI
In this interview from theCUBE + NYSE Wired: Robotics & AI Infra Leaders 2026, Darren Chien, managing director of APAC at Positron AI, joins theCUBE's John Furrier to discuss why inference has become the defining economic battleground of the AI era. Chien breaks down the mechanics of prefill and decode, explaining how compute-bound and memory-bound workloads require different optimization strategies. He details how Positron AI is built to drive down the cost of tokens, competing on tokens per dollar and tokens per watt rather than raw throughput alone. Chien ... Read more
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