Chip Talk > NVIDIA and TSMC Set to Redefine High-Performance Computing with 2nm A16 Node
Published September 15, 2025
NVIDIA, the Silicon Valley giant known for its pioneering graphics processing technology, is reportedly set to break from tradition by adopting TSMC's cutting-edge A16 2nm-class node for its upcoming Feynman architecture, according to a report by Commercial Times. This move, expected around 2028, would mark a departure from NVIDIA's usual strategy of leveraging more mature nodes for its graphics processing units (GPUs). Typically, companies like AMD have been quicker to adopt newer process nodes for high-performance computing (HPC) applications. However, the shift is likely a strategic play to maintain NVIDIA's competitive edge against rivals like AMD.
The allure of TSMC's A16 node lies in its technological advantages. This advanced process incorporates cutting-edge features such as nanosheet transistors and TSMC’s Super Power Rail (SPR) solution, promising an 8–10% speed gain and a 15–20% reduction in power consumption when compared to its predecessors. These improvements are not just incremental; they offer significant enhancements necessary for the AI-driven applications that will form the backbone of NVIDIA's Feynman architecture. It’s worth noting that while TSMC's 2nm node represents a leap forward, it comes with a hefty price tag—reported costs are upwards of $30,000 per wafer for NVIDIA's specific version of this process.
Historically, smartphone makers have spearheaded the adoption of new process technologies, leveraging the need for more efficient and faster chips in consumer electronics. However, NVIDIA's move indicates the rising importance of artificial intelligence applications in semiconductor technology development. By being an early adopter, NVIDIA positions itself as a vanguard of AI-enhanced HPC solutions. This also reflects the growing trend of AI technologies pushing the boundaries of semiconductors, where efficiency and performance are paramount to support demanding AI models.
Demand for TSMC's advanced nodes is growing, despite, or perhaps because of, their high cost. A wide range of industries are interested in the performance benefits these technologies offer, emphasizing TSMC’s strategic role in the semiconductor ecosystem. Moreover, reports indicate that NVIDIA's decision could spur a rapid escalation in orders, creating a ripple effect across downstream supply chains, particularly in testing and verification processes. Institutional investors are keenly watching this space, as increased demand for system-level testing (SLT) and other verification technologies are predicted, underscoring the complexity and scale of AI semiconductor deployment.
NVIDIA’s potential leap to TSMC’s 2nm A16 node for its Feynman architecture could represent a defining moment in high-performance computing. As the semiconductor industry continues to grapple with the challenges of integrating AI at its core, early adoption of such advanced nodes might just set the tone for the future of chip design and manufacturing. While the financial implications—both cost and investment—are significant, the strategic benefits could secure NVIDIA’s position as a leader in AI-driven semiconductor technology for years to come.
Join the world's most advanced semiconductor IP marketplace!
It's free, and you'll get all the tools you need to discover IP, meet vendors and manage your IP workflow!
No credit card or payment details required.
Join the world's most advanced AI-powered semiconductor IP marketplace!
It's free, and you'll get all the tools you need to advertise and discover semiconductor IP, keep up-to-date with the latest semiconductor news and more!
Plus we'll send you our free weekly report on the semiconductor industry and the latest IP launches!
