Qualcomm Forecasts Billions in Additional Revenue from New Data Center Solutions

2026-06-25 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

Qualcomm is aggressively expanding into the AI data center market with a new strategy encompassing networking, AI accelerators, and custom/standard CPUs, projecting significant revenue growth. Following its May 2025 announcement of Humain as its first data center customer for Arm-compatible Oryon CPUs, Qualcomm acquired Alphawave Semi for \$2.4 billion and Modular for \$3.9 billion. These acquisitions bolster its capabilities in high-speed interconnects, advanced packaging, custom silicon services, and hardware-agnostic AI software. Qualcomm's "Dragonfly" brand will introduce connectivity solutions up to 800G today and 1.6T by late 2026, custom silicon in 2027, and C1000 server CPUs with up to 250 Oryon cores at 5GHz in 2028. The company forecasts its data center segment will add \$5 billion to revenue in 2027 and \$15 billion in 2029, contributing to a non-handset revenue increase from \$22 billion in 2026 to \$40 billion in 2029.

Key takeaway

For CTOs evaluating future data center infrastructure, Qualcomm's aggressive entry with rack-scale solutions, including Oryon CPUs and HBC memory, presents a new competitive option. You should assess their Dragonfly product roadmap, especially the C1000 CPUs and AI250 accelerators, for potential performance and power efficiency advantages in AI decoding workloads. Consider how their hardware-agnostic software strategy could integrate into your heterogeneous environments, potentially diversifying your vendor reliance beyond traditional players.

Key insights

Qualcomm is rapidly building a comprehensive rack-scale data center solution through strategic acquisitions and new product development.

Principles

• Rack-scale solutions require diverse component integration.
• High-speed interconnects are critical for AI clusters.
• Power efficiency drives data center cost per token.

Method

Qualcomm's strategy involves acquiring interconnect and software expertise, then rolling out connectivity, custom silicon, and high-performance CPUs with HBC memory.

In practice

• Integrate custom silicon for specialized workloads.
• Utilize chiplet technology for server CPUs.
• Prioritize in-package LPDDR for memory bandwidth.

Topics

• AI Data Centers
• Qualcomm Dragonfly
• Oryon CPUs
• High-Bandwidth Compute
• Rack-Scale Solutions
• Semiconductor Acquisitions

Best for: VP of Engineering/Data, Director of AI/ML, AI Architect, Executive, Investor, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.