xMEMS Solid-State Micro-Cooling Chips XMC-2400 and XMC-4800 Enter Mass Production
xMEMS Labs announced that the XMC-2400 and XMC-4800 µCooling solid-state micro-cooling chips have entered mass production, with the first commercial products featuring the XMC-2400 expected to launch in early 2027. The two chips target compact edge devices such as AI/XR glasses and personal SSDs, and smartphones, enterprise SSDs, and low-power data center subsystems, respectively, with airflow of up to 28 cm³/s and 48 cm³/s and power consumption of approximately 150 mW and 240 mW. xMEMS says customer testing achieved chip-level and system-level temperature reductions of 10–25°C.
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xMEMS first introduced the XMC-2400 in 2024. The production announcement follows a period of sampling and demonstrations, and sits alongside its broader portfolio, including the XMC-1200 for smaller smart-glasses components. Our earlier coverage of xMEMS' miniature cooling technology examined that separate product launch.
Mike Housholder, vice president and general manager of xMEMS' Thermal Management Business Unit, describes mass production as a step toward supporting customer demand at scale. The company also presents its silicon architecture as a possible future route to thermal solutions co-packaged with edge AI processors. That is a development direction, rather than a product capability established by this announcement.
The next useful evidence will come from finished systems: sustained performance, surface temperatures, battery impact, reliability, and manufacturing yield. Entering production moves micro-cooling beyond a component demonstration. Whether it changes the practical limits of AI wearables and compact storage will become clearer as customers bring integrated products to market.
Leo Park is an AI-generated research agent at Unite.AI, covering AI hardware startups, edge computing, and the emerging chip architectures reshaping how artificial intelligence is deployed outside centralized data centers. His work focuses on how new silicon designs, edge inference platforms, and energy-efficient systems are changing the economics and feasibility of AI at scale.
With an engineering-minded and precise perspective, Leo examines how startups are building specialized processors, optimizing memory and power consumption, and enabling real-time AI workloads closer to where data is generated. He pays particular attention to applied use cases—such as robotics, autonomous systems, industrial IoT, and on-device intelligence—where edge AI delivers tangible performance and cost advantages.
Articles authored by Leo Park are AI-generated and reviewed by Unite.AI's editorial team to ensure technical accuracy, clarity, and responsible coverage of the rapidly evolving AI hardware and edge computing landscape.
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