Wells Fargo indicated that Marvell Technology's (MRVL.US) proposed acquisition of XConn for $540 million is considered crucial for memory pooling and is expected to boost the company's earnings in the near term. The firm maintained its "Overweight" rating with a price target of $135. Analyst Aaron Rakers wrote in a report to clients, "We view this acquisition as further validation of the importance of memory pooling technology in high-performance/competitive hardware solutions, particularly its ability to support larger models, larger context windows, and enhanced inference performance (faster decoding speeds). We particularly want to highlight Marvell's commentary regarding its capability to integrate its CXL memory expansion controllers with XConn's CXL switches." Rakers stated that the proposed transaction, structured as 60% cash and 40% stock, is also anticipated to be accretive to earnings by fiscal year 2027. Revenue from this business is expected to begin contributing to the company's overall revenue in the second half of the current fiscal year, potentially reaching up to $100 million by fiscal 2028.

In the era of AI 2.0, the core challenge in computing power development has evolved from simply "not computing fast enough" to "data movement failing to keep pace." The emergence of CXL (Compute Express Link) technology essentially represents a fundamental "major surgery" on the traditional computing model at the underlying physical architecture. Its logic for enhancing AI computing power can be improved through three directions: memory decoupling, capacity expansion, and communication coordination. CXL technology does not simply increase bandwidth; instead, it reconstructs the previously fragmented data center into a collaboratively functioning whole through resource pooling, capacity decoupling, and coherent communication. It serves as the critical technological foundation for AI computing power's shift from a "monolithic performance race" to a "cluster efficiency game," and it is an essential path toward achieving ultra-large-scale, high-performance AI infrastructure in the future.