Detailed Narrative
AI Custom Silicon Expansion
Broadcom is expanding its custom AI accelerator engagements, now working with four additional hyperscalers beyond the initial three. These new partners are developing their own customized XPUs, with the first tape-outs expected this year. While these new engagements are not yet included in the previously estimated $60-$90 billion serviceable addressable market for 2027, they represent significant future growth potential as hyperscalers race towards 1 million XPU clusters.
Next-Gen AI Hardware Innovation
The company is pushing the technological envelope in AI hardware, tapping out the industry's first 2-nanometer AI XPU with 3.5D packaging, aiming for a 10,000 teraflops XPU. Concurrently, Broadcom has tapped out its next-generation 100-terabit Tomahawk 6 switch, featuring 200G serdes and 1.6 terabit bandwidth, with samples expected within months to enable AI clusters to scale up to 1 million XPUs on Ethernet.
VMware Integration and Private AI Foundation
The infrastructure software segment saw substantial growth, driven by the conversion of perpetual licenses to subscriptions (over 60% complete) and upselling customers to the full-stack VCF for data center virtualization. Approximately 70% of the largest 10,000 customers have adopted VCF. Broadcom, in collaboration with NVIDIA, has also launched the VMware Private AI Foundation, which virtualizes GPUs on a common platform for on-prem AI workloads, already securing 39 enterprise customers.
Non-AI Semiconductor Trends
The non-AI semiconductor business experienced a slow recovery in Q1, with revenue down 9% sequentially. While broadband showed a double-digit sequential recovery and server storage is expected to improve in Q2, enterprise networking remains flat due to channel inventory. Industrial resales are declining. Overall, non-AI semiconductor revenue is expected to be flattish sequentially in Q2, despite growing bookings.
Strategic R&D Investment
Broadcom is significantly increasing its R&D investments, particularly in leading-edge AI semiconductors. This focus is on developing advanced accelerators and networking solutions to meet the evolving demands of hyperscale customers for larger and more efficient AI clusters, aligning with their roadmaps for future XPU deployments. The R&D spend was $1.4 billion in Q1 and is expected to increase in Q2.