Nvidia Q1 Earnings Preview: Vera Rubin and Networking Double Click, Will It Crush Expectations Again?
NVIDIA is set to report Q1 FY2027 earnings on May 20, with market revenue expectations around $71.8 billion. Analysts predict strong performance driven by its data center business, which saw 75% YoY growth in Q4 FY2026. The company's new Vera Rubin platform aims to address inference costs and competition from Intel and AMD. NVIDIA’s networking segment also showed significant growth, up 142% YoY in FY2026. Key risks include intensifying competition, hyperscale providers diversifying supply chains, and the sustainability of AI infrastructure capital expenditures. Investor focus will be on Rubin platform adoption and networking segment acceleration.
TradingKey - NVIDIA ( NVDA) is set to report its first-quarter results for fiscal year 2027 after the U.S. market close on May 20. The market generally expects quarterly revenue of approximately $71.8 billion, but several investment banks believe this forecast is too conservative. Among them, Citigroup ( C) projects revenue to reach $73 billion, while Wells Fargo ( WFC) has provided an even more optimistic forecast of $80.4 billion.
Looking back at its historical performance, NVIDIA has demonstrated strong resilience by consistently exceeding expectations. In nine of the past 12 quarters, its revenue beat market estimates by more than $1 billion, with the third and fourth quarters of fiscal 2026 exceeding forecasts by $2 billion and $1.9 billion, respectively. This explosive performance is primarily driven by the robust growth of its Data Center business, which recorded revenue of $62.3 billion in the fourth quarter of fiscal 2026—a 75% year-over-year increase—accounting for more than 90% of total revenue.
CEO Jensen Huang has repeatedly emphasized that global demand for AI computing power is experiencing "exponential growth," and NVIDIA's GPU products serve as the core infrastructure for this wave.
Since the end of 2023, driven by the sustained demand for GPUs and the rapid expansion of its Data Center business, the company's stock price has surged nearly sixfold, briefly making it the world's most valuable publicly traded company.
However, NVIDIA also faces intensifying market competition and pressure from technological iteration. As hyperscale cloud service providers actively promote the diversification of their computing power supply chains, Intel ( INTC ), AMD ( AMD ), and other competitors are accelerating the launch of their AI chip products. At the same time, the AI industry is shifting from the model training phase toward inference optimization, placing higher demands on the power efficiency and specialization of chips.
In response, NVIDIA has launched its next-generation Vera Rubin platform, which reportedly reduces inference costs tenfold. Initial cloud service customers include giants such as AWS and Microsoft ( MSFT) Azure. In the upcoming earnings report, investors may focus on the commercialization progress of this platform as well as the growth of the networking business.
Vera Rubin: Market Anticipates Highlights
NVIDIA's Vera Rubin rack-scale system, announced in January and officially released in March, is being viewed by the market as a key vehicle for sustaining its growth momentum amid intensifying competition in the AI chip sector.
The company defines this platform as an "extreme co-design" six-chip architecture that integrates the Vera CPU, Rubin GPU, NVLink 6 switch, ConnectX-9 SmartNIC, BlueField-4 DPU, and Spectrum-6 Ethernet switch, aiming for breakthrough efficiency gains such as a roughly 90% reduction in inference token costs and a 75% decrease in GPU usage for equivalent compute requirements through system-level optimization.
Following the market validation of the Blackwell series, Rubin is positioned as NVIDIA's next-generation flagship AI acceleration platform. CEO Jensen Huang has publicly stated that cumulative sales for the Blackwell and Rubin product lines are expected to reach the $1 trillion level by the end of 2027.
Currently, the market's focus is on whether Rubin can successfully transition from its product roadmap to actual customer deployment. According to reports, NVIDIA has finalized mass production plans with its ODM partners, with trial production set for June and initial deliveries to top-tier North American cloud service providers starting in July, including Microsoft, Google ( GOOGL ), Amazon ( AMZN ), Meta ( META ), and Oracle ( ORCL) and other tech giants who will be among the first customers—these firms contribute nearly half of NVIDIA's revenue, and their capital expenditure plans directly dictate NVIDIA's performance outlook.
"Computing demand is growing exponentially—the tipping point for intelligent AI has arrived." "Grace Blackwell and NVLink are the kings of inference today—reducing cost per token by an order of magnitude—and Vera Rubin will further solidify this lead," said founder and CEO Jensen Huang.
In the upcoming earnings call, NVIDIA's guidance on Rubin-related sales will be a critical variable for the stock price. Investors are especially concerned with whether Rubin can become the primary driver of revenue in fiscal 2027, or if it will take until fiscal 2028 to unleash its full potential.
Network business growth accelerates
Today, NVIDIA's networking business is rising at a staggering pace. By efficiently connecting GPUs and DPUs within large-scale data centers, this segment has emerged as one of the company's fastest-growing and most significant profit contributors. In fiscal year 2026, networking revenue surged 142% year-over-year to $31 billion, accounting for approximately 15% of the company's total revenue—a scale that already exceeds the overall size of many independent semiconductor firms.
In terms of its product portfolio, NVIDIA has built a comprehensive suite of solutions covering NVLink, Quantum InfiniBand, Spectrum-X Ethernet, as well as supporting processors and software. The core objective is to create low-cost, energy-efficient underlying communication networks for AI infrastructure. This February, NVIDIA partnered with Meta, which will fully adopt the Spectrum-X Ethernet platform for AI training and inference scenarios, further validating the industry recognition of its networking products.
The explosive momentum of the networking business is even more apparent in the $62.3 billion data center revenue for the fourth quarter of fiscal 2026. Within this, the compute business (primarily GPUs) recorded $51.3 billion in revenue, up 58% year-over-year and 19% quarter-over-quarter. Meanwhile, networking revenue surpassed $11 billion for the single quarter, soaring 263% year-over-year and 34% sequentially—a scale that already exceeds the annual revenue of most independent semiconductor companies.
According to reports, Foxconn's delivery of all-optical CPO switch racks to NVIDIA has been moved forward, and shipment expectations have been revised upward from over 10,000 units for the full year 2026 to more than 50,000 units combined for 2026-2027. This signal indicates that NVIDIA is transitioning from a standalone GPU supplier to a provider of comprehensive AI computing network solutions, with the networking business becoming a vital pillar for the company's long-term growth.
Risk Factors
When examining NVIDIA's growth prospects, it is also necessary to carefully evaluate potential risk factors.
On the competitive front, Google parent Alphabet's positioning in the customized accelerator chip field poses the most direct challenge. As its TPU architecture continues to iterate and its collaboration with Broadcom in design and manufacturing deepens, some general-purpose computing demand may potentially be displaced.
Meanwhile, Amazon has gradually integrated its self-developed Trainium chips into AWS internal workloads. While this reflects strong demand for AI infrastructure among cloud providers in the short term, over the long run, if customers prefer more cost-effective proprietary solutions, it could exert marginal pressure on NVIDIA's market share.
Notably, AI startup Anthropic has significantly increased its procurement ratio of TPUs and Trainium chips, a move that may signal an acceleration in the implementation of customer diversification strategies.
Deeper concerns focus on the sustainability of capital expenditures; whether the massive investments by hyperscalers can translate into commensurate commercial returns remains a core market concern. If the pace of monetization for end-user applications lags behind the rate of infrastructure expansion, or if the macro environment leads to downward revisions in growth expectations for cloud providers, investors may re-evaluate the sustainability of the current AI investment cycle, potentially triggering a recalibration of valuation frameworks.
In addition, controversy surrounding "circular AI trades" continues in the market. Such arrangements refer to the capital and business closed loops formed between cloud service providers, model developers, and computing power providers, which strengthen ecosystem ties through mechanisms like cross-shareholding and preferential procurement.
NVIDIA has participated in several similar collaborations, including strategic investments in companies such as OpenAI and CoreWeave. On the surface, this model helps stabilize order visibility and supports valuation logic, but critics worry that if genuine end demand fails to keep pace, the circulation of funds within the system could amplify cyclical volatility.
This content was translated using AI and reviewed for clarity. It is for informational purposes only.
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