Microsoft unveils the Maia 200 AI inference chip; SK Hynix's exclusive HBM3E technology powers a significant leap in computing power
On January 28, 2026, Microsoft officially launched its second-generation self-developed AI inference chip, the Maia 200. Leveraging TSMC's 3nm process and SK Hynix's exclusively supplied 12-layer stacked HBM3E memory, the chip achieves a dual breakthrough in performance and energy efficiency. This chip will be primarily used for the deployment of the GPT-5.2 model, significantly improving AI inference efficiency and reigniting global demand for HBM memory and AI servers, leading to a surge in orders for related components across the supply chain.
As a core upgrade in Microsoft's self-developed chip strategy, the Maia 200 achieves a leap forward in hardware specifications. The chip utilizes TSMC's cutting-edge 3nm manufacturing process, integrating over 140 billion transistors. Within a 750-watt power consumption range, it achieves a computing power exceeding 10 petaFLOPS at FP4 precision, three times the performance of Amazon's third-generation Trainium chip. Its FP8 precision performance also surpasses Google's seventh-generation TPU, and it is defined by Microsoft as "the most efficient inference system ever deployed."
One of its core competitive advantages stems from deep cooperation with SK Hynix. The Maia 200 is equipped with 216GB of HBM3E memory, composed of six 36GB 12-layer stacked chips, providing a memory bandwidth of up to 7 TB/s. This HBM3E product is the world's first 12-layer stacked solution, which SK Hynix was the first to mass-produce in September 2024. By reducing the thickness of a single DRAM chip by 40%, it achieves a 50% increase in capacity at the same thickness as the previous generation 8-layer product, while also optimizing heat dissipation performance by 10% through advanced cooling technology, perfectly matching the high-load operating requirements of AI chips.
It is reported that the Maia 200 will be first deployed in Microsoft's data centers in Iowa and Phoenix, providing computing power support for the GPT-5.2 large model and enterprise-level Copilot assistant. The head of Microsoft's cloud and AI business stated that the chip offers a 30% improvement in performance per dollar compared to current hardware and uses an Ethernet interconnect architecture, allowing for seamless scaling in clusters of up to 6144 accelerators, providing a cost-effective solution for large-scale AI inference tasks and further reducing reliance on Nvidia GPUs. This release has directly fueled a surge in demand for AI servers and core components. Currently, global demand for AI server memory is 8-10 times that of traditional servers, and with HBM3E being a core component for high-end AI chips, the supply-demand gap continues to widen. SK Hynix's 12-layer HBM3E product is already completely sold out for 2025-2026, and coupled with Samsung and Micron shifting their production towards high-end memory, the premium on HBM memory will persist. It is expected that the Q1 HBM3E price will exceed $500.
The ripple effect across the supply chain is also evident, with a surge in orders for AI server motherboards and high-speed interconnect components. The high bandwidth memory requirements of the Maia 200 are driving upgrades in motherboard designs to accommodate HBM3E, while also demanding higher transmission efficiency from high-speed interconnect components such as MCIO cables. Related manufacturers are seeing significant month-on-month increases in orders. Analysts predict that with Microsoft's Maia series chip purchases expected to reach 400,000 units by 2026, this will continue to drive demand in the upstream hardware supply chain.
Industry insiders point out that the launch of the Maia 200 intensifies competition in the AI chip market and also confirms the technological barriers and scarcity of HBM memory. Against the backdrop of explosive demand for AI large model inference, HBM3E and subsequent HBM4 products will become the core drivers of computing power competition. SK Hynix, with its exclusive supply advantage, is expected to further consolidate its leading position in the AI memory market, and the entire AI hardware supply chain will usher in structural upgrade opportunities.