Qualcomm told investors on June 24, 2026, it will generate over $15 billion in annual datacenter AI chip revenue by fiscal 2029, tripling prior guidance of $5 billion and placing the company squarely in NVIDIA's inference lane. The commitment arrives with anchor orders from Meta and Microsoft, but the silicon—Arm-based inference accelerators targeting large language model deployment—remains in preproduction. Volume shipments are scheduled for late 2027, leaving a 24-month execution window against the industry's most entrenched supplier.

The guidance rests on Qualcomm's Modular acquisition, completed in early 2025 for an undisclosed sum believed to exceed $800 million, and a proprietary memory architecture called HBC that the company claims delivers 40% lower latency than NVIDIA's HBM3e configuration in token-generation workloads. The C1000 CPU, an Arm v9 design with 128 cores and integrated inference engines, will anchor the platform. Meta has publicly committed to a multi-year deployment supporting Llama 4 inference at scale; Microsoft's agreement covers Azure AI services across 12 regions by mid-2028. Neither customer disclosed contract values, but industry comps suggest Meta's commitment alone approaches $3 billion over three years.

The move reshapes datacenter capex allocation in two directions. First, it validates Arm as a credible alternative to x86 and NVIDIA's CUDA monopoly in production AI workloads, not just edge inference. Meta's participation is the signal: the company spent $38 billion on infrastructure in 2025 and has shown zero tolerance for vendor lock-in. Second, it opens a $22 billion addressable market in inference-specific silicon by 2029, per Qualcomm's internal TAM model, distinct from training-focused GPUs. If Qualcomm captures even 68% of its stated target, it will rank as the second-largest AI silicon supplier by revenue, behind NVIDIA but ahead of AMD and Intel combined. The risk is execution. The company has no datacenter silicon track record, and its fiscal 2026 datacenter revenue sits at $420 million—a 36x scale-up in three years.

Allocators should watch three markers. First, preproduction tapeout milestones for the C1000 in Q4 2026; any slip past January 2027 compresses the customer validation window. Second, Meta's Llama 4 inference deployment in Q2 2027, the first public proof point for HBC memory performance claims. Third, NVIDIA's response in inference-specific SKUs; the company has historically used Hopper and Blackwell for both training and inference, but a dedicated inference product line would directly contest Qualcomm's cost-per-token positioning. Microsoft's Azure commitment includes a performance benchmark: Qualcomm silicon must deliver 15% better performance-per-watt than incumbent solutions by Q3 2027, or the contract allows for renegotiation.

Qualcomm's stock closed at $187.40 on June 26, 2026, up 11.2% over two sessions. The options market priced in a 28% implied move through fiscal 2027 earnings, and September 2027 calls at the $220 strike saw volume exceed 18,000 contracts. The capital markets are pricing in success, but the chips ship in 18 months.