Semiconductor research firm SemiAnalysis expects Nvidia’s data center revenue for the second half of fiscal 2027 to exceed consensus estimates by about 20%, supported by easing supply constraints and strong production capacity.
The upgraded forecast reflects improving availability of next-generation HBM4 memory and sufficient wafer supply, two bottlenecks that had previously limited shipments. As these constraints ease, SemiAnalysis says Nvidia’s Rubin platform is now positioned for a significant ramp-up after earlier delays tied to memory shortages.
The projection is based on supply chain data spanning raw materials, wafer fabrication, component manufacturing, server assembly, and cloud procurement, offering a more detailed view than typical market models.
Nvidia outlook lifted by supply recovery
Rubin Ultra scaled back after design hurdles
Despite the stronger revenue outlook, SemiAnalysis also revealed that Nvidia scrapped its original four-chip Rubin Ultra design just three months after unveiling it at GTC 2026. The replacement has been reduced to roughly half the size and performance.
The change was driven by ongoing challenges in advanced packaging, a critical technology required to integrate multiple chips and high-bandwidth memory into a single system. Industry constraints, including limited advanced packaging capacity, continue to act as a structural bottleneck.
This combination of production progress and design compromise highlights a mixed trajectory: supply improvements are accelerating shipments, but engineering complexity is reshaping product ambitions.
Custom chips intensify competitive pressure
At the same time, competitive dynamics are shifting as major cloud providers and AI developers expand their use of custom-built ASICs. SemiAnalysis notes that companies are increasingly deploying multi-vendor hardware strategies rather than relying on a single ecosystem.
Anthropic illustrates this trend. Its Claude models are primarily trained on Google TPUs, while inference workloads are moving toward Amazon Trainium systems. Nvidia GPUs remain in use for broader research and general-purpose computing, but no longer dominate every layer of the stack.
This shift, which SemiAnalysis says would have been unlikely a year ago, signals that proprietary chips are beginning to erode Nvidia’s long-standing CUDA advantage.
Market enters more fragmented phase
The broader AI hardware market is becoming more complex as both supply chain constraints and customer behavior evolve. SemiAnalysis will continue tracking this shift through its Accelerator Model and HBM Model, covering companies including AMD, Broadcom, and Marvell.
The firm concludes that the balance between general-purpose GPUs and specialized ASICs will be a defining factor in global data center deployment. While Nvidia stands to benefit from near-term supply recovery, rising competition and technical challenges are likely to shape a more fragmented and competitive landscape.
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