Marvell has confirmed plans to acquire Celestial AI in a deal worth up to $5.5 billion, a figure that immediately places it among the most aggressive acquisitions any mid-tier silicon vendor has made in the current AI cycle, and marks a decisive shift in how the company intends to compete against Nvidia, AMD, and Intel in the rest of the decade.
Celestial AI spent much of the last four years building a photonic interconnect platform intended to deliver high-bandwidth communication among accelerators without the electrical-signal penalties that limit today’s GPU-dense racks.
Its Orion architecture centres on light-based data movement between local compute domains, a design intended to expand usable memory and reduce power lost to traditional SerDes. Those capabilities explain why Marvell, which already ships a large portfolio of cloud-scale networking silicon, is positioning the acquisition as a way to collapse the boundary between server-to-server networking and on-package connectivity inside AI nodes.
The scale of the deal is pretty huge. For comparison, AMD bought Pensando for about $1.9 billion in 2022 to accelerate its data-processing-unit portfolio, while Nvidia’s strategy has relied on internal development and targeted acquisitions like Mellanox, which cost $6.9 billion but delivered an immediate foothold in high-performance networking.
Higher bandwidth and larger memory pools
(Image credit: Nvidia)
Training and inference clusters built around accelerators such as Nvidia Blackwell are fundamentally constrained by bandwidth. Even with advanced SerDes and high-speed electrical links, the growth of model sizes is pressing against the limits of conventional interconnect design. Celestial’s approach replaces long-reach electrical paths with photonic waveguides that can sustain high throughput at lower power, while maintaining signal integrity across the many parallel channels now required by large-model workloads.
Marvell already supplies cloud providers with optical DSPs, PAM4 transceivers, and network-interface controllers. Bringing Celestial’s optical compute-fabric architecture into that portfolio creates an opportunity to span the hierarchy from server-rack switching down to chip-adjacent links. If the company can commercialize Celestial’s technology at scale, it could offer an alternative path to deploy large accelerator clusters without relying solely on GPU-centric fabrics. That shifts the company from a supplier of supporting components into a core enabler of system architecture.
Celestial’s technology also intersects directly with the memory debate in AI. Today’s accelerators hinge on ever-larger pools of HBM, a trend that pushes cost, thermals, and packaging complexity upward. Photonic interconnects promise to extend memory coherence over greater distances, allowing external memory and disaggregated resources to behave more like local pools. It is a best-case scenario that depends on manufacturing maturity, but it hints at why Marvell is investing so heavily. If coherent optical fabrics reach production scale, they could change the balance between compute and memory in future data center designs.
Positioning against Nvidia, AMD, and Intel
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