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U.S. stock optical module leader Marvell announced an all-stock acquisition of optical chip leader Lumentum, with a consideration of approximately 115 billion USD

According to Marvell's announcement, the company has reached a final agreement to acquire Lumentum in an all-stock transaction.The agreement stipulates that Lumentum shareholders will receive 4.500 shares of Marvell common stock for each share they own, implying an enterprise value of approximately $115 billion for Lumentum, equivalent to about $1132 per share, representing a premium of approximately 23.9% over the closing price on October 28, which was unaffected by rumors. After the transaction is completed, existing Marvell shareholders will hold about 67%, while Lumentum shareholders will hold about 33%.Both parties stated that the merger will integrate Marvell's capabilities in electrical and optical interconnects, switching, and custom chips, along with Lumentum's strengths in high-performance lasers, optical transceiver modules, and optical circuit switching, aiming to create a full-stack connectivity portfolio for AI infrastructure and expand the addressable market size to approximately $550 billion.The transaction is expected to achieve over $1 billion in annualized operating cost synergies and is anticipated to enhance Marvell's non-GAAP earnings per share in the first year post-closing. The closing is expected in the first half of 2028, pending approval from both parties' shareholders and regulators. After the closing, Matt Murphy will continue to serve as Chairman and CEO, and three directors from Lumentum, including CEO Michael Hurlston, will join Marvell's board of directors.

Marvell launches AI "memory decoupling" architecture to address the bandwidth bottleneck of Agentic AI inference

According to official news, Marvell Technology announced the launch of a new generation of memory solution portfolio for AI infrastructure, covering server-level AI storage, rack-level CXL memory expansion and pooling, as well as multi-rack optical interconnect shared memory, aimed at addressing the growing memory capacity and bandwidth bottlenecks in Agentic AI inference processes.Marvell stated that as AI model sizes increase, context windows extend, and KV Cache demand grows, traditional tightly coupled architectures of computing and memory are limiting AI inference efficiency. Through memory disaggregation, memory resources can be made more independent of computing resource expansion, improving GPU utilization and reducing data movement latency. The products released include:Bravera SC6 PCIe 6.0 SSD controller: Designed for AI inference storage scenarios, it helps cloud service providers migrate more KV Cache to high-performance SSDs, enhancing infrastructure efficiency. This product features an architecture compatible with multi-vendor NAND and is expected to begin sampling in the fourth quarter of 2026.Marvell Structera X memory expansion solution: Based on CXL technology, it supports rack-level memory expansion and resource pooling, helping data centers share and allocate memory resources more flexibly, reducing AI infrastructure costs.Marvell Photonic Fabric optical interconnect memory solution: Constructs a shared memory architecture across multiple racks using optical interconnect technology, supporting up to 32TB warm KV Cache offloading and helping AI inference clusters enhance throughput capacity.Marvell stated that the Photonic Fabric solution can achieve a 2 to 3 times increase in Token throughput under existing data center space and power consumption constraints, supporting larger scale models and longer context AI applications.Marvell executive Will Chu stated that AI infrastructure is transitioning from a single server architecture to a system where computing, memory, and connectivity operate in synergy, and in the future, memory needs to expand more independently to enhance resource utilization and Token efficiency.As the demand for AI Agents and large model inference continues to grow, memory capacity, bandwidth, and data transfer efficiency are becoming new focal points of competition in AI infrastructure, following computing power.
2026-08-04
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