During a CNBC interview, AMD Chair and CEO Lisa Su outlined a vision for AI computing that expects more than 5 billion active AI users within five years. She described the transition to “yotta-scale” infrastructure that will drive global compute capacity from today’s 100 zettaflops to over 10 yottaflops by 2030. Su highlighted AMD’s collaborations with OpenAI, AstraZeneca and Blue Origin as early proof points and reiterated a $150 million commitment to expand AI education in classrooms and communities, signaling both near-term revenue opportunities and long-term market development.

At CES 2026, AMD introduced its Ryzen AI 400 Series processors for PCs and laptops, featuring 12 CPU cores, 24 threads and a neural processing unit delivering up to 60 TOPS. The new line achieves 1.3x faster multitasking and 1.7x faster content creation versus the prior generation, according to AMD benchmarks. Senior VP Rahul Tikoo reported that OEMs have certified more than 250 AI-enabled PC platforms—double last year’s count—and that systems powered by these chips will ship in Q1 2026.

AMD further deepened its data-center strategy with the preview of the Helios rack-scale platform, capable of delivering up to 3 AI exaflops per rack by combining Instinct MI455X accelerators, EPYC “Venice” CPUs and Pensando “Vulcano” NICs under the open ROCm software stack. The launch of the MI440X GPU, optimized for on-premise enterprise use in eight-GPU trays, and a roadmap to next-generation MI500 Series chips—promising a 1,000x performance uplift by 2027—positions AMD to narrow the gap with larger incumbents.

At CES, thermal solutions partner Ventiva unveiled an AI-Ready Laptop reference design built around the latest AMD Ryzen mobile processor operating at 44.3 W total platform power in a sub-16 mm chassis. The design employs Ventiva’s Zoned Cooling™ modules—three 62 mm units—which reclaim up to 7,200 mm² of motherboard real estate and support a 77 Whr battery and full-size SSD. By eliminating conventional fans and heat pipes, this modular approach reduces system cost, enables thinner form factors and ensures sustained AI performance for on-device inference workloads.