Nvidia Q1 Earnings Preview: Rubin Mass Production and Networking Dual Boost, Will It Crush Expectations Again?

TradingKey - NVIDIA ( NVDA) is scheduled to release its first-quarter fiscal 2027 earnings report after the U.S. market close on May 20. The market generally expects revenue of approximately $71.8 billion for the quarter, but several investment banks believe this estimate is too conservative. Among them, Citi ( C) forecasts revenue will reach $73 billion, while Wells Fargo ( WFC) has provided an even more optimistic projection of $80.4 billion.

Reviewing historical performance, NVIDIA has demonstrated strong resilience by consistently exceeding expectations. In 9 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 growth is primarily driven by the robust performance of its data center business, which generated $62.3 billion in revenue in the fourth quarter of fiscal 2026—a 75% year-over-year increase—accounting for over 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 of this trend.

Since late 2023, buoyed by sustained robust 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 iterations. As hyperscale cloud service providers actively push for diversification in their computing power supply chains, Intel ( INTC ), AMD ( AMD ), and other competitors are accelerating the rollout of their AI chip products. Meanwhile, the AI industry is shifting from the model training phase to inference optimization, which places higher demands on the energy 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 will likely focus on the commercial progress of this platform and the growth of the networking business.

Vera Rubin is a key business highlight.

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 growth momentum amid intensifying competition in the AI chip space.

Defined by the company as an "extreme co-design" six-chip architecture, the platform integrates the Vera CPU, Rubin GPU, NVLink 6 switch, ConnectX-9 SmartNIC, BlueField-4 DPU, and Spectrum-6 Ethernet switch, aiming to achieve breakthrough efficiency improvements through system-level optimization, reducing inference token costs by approximately 90% and GPU usage by about 75% for the same compute requirements.

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 combined sales of the Blackwell and Rubin product lines are expected to reach the $1 trillion mark by the end of 2027.

The current market focus is on whether Rubin can successfully transition from the product roadmap to actual customer deployment. According to reports, NVIDIA has finalized mass production plans with ODM partners, with pilot production set to begin in June and the first batch of products to be delivered to major North American cloud service providers in July. Tech giants such as Microsoft, Google ( GOOGL ), Amazon ( AMZN ), Meta ( META ), and Oracle ( ORCL) will be among the first customers—these companies contribute nearly half of NVIDIA's revenue, and their capital expenditure plans directly dictate NVIDIA's financial performance trajectory.

"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 extend 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 particularly focused on whether Rubin can become a primary revenue driver in fiscal 2027 or if its full potential will not be unleashed until fiscal 2028.

Network business growth accelerates

Today, NVIDIA's networking business is ascending 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 centers. In fiscal 2026, networking revenue soared 142% year-over-year to $31 billion, accounting for roughly 15% of total revenue—a scale that already surpasses the total business size of many standalone semiconductor firms.

In terms of its product matrix, NVIDIA has established a comprehensive suite of solutions spanning NVLink, Quantum InfiniBand, and Spectrum-X Ethernet, alongside supporting processors and software. The core focus is on building low-cost, energy-efficient underlying communication networks for AI infrastructure. This February, NVIDIA partnered with Meta, which will fully deploy the Spectrum-X Ethernet platform across AI training and inference workloads, further validating the industry's acceptance of its networking portfolio.

Data Center revenue of $62.3 billion in the fourth quarter of fiscal 2026 highlights the explosive momentum of the networking business. Within this, the compute segment (primarily GPUs) brought in $51.3 billion, up 58% year-over-year and 19% sequentially, while networking revenue for the single quarter broke past $11 billion, surging 263% year-over-year and 34% quarter-over-quarter. This scale has already eclipsed the annual revenue of most independent semiconductor companies.

Reports indicate that Foxconn has delivered all-optical CPO switch racks to NVIDIA ahead of schedule, with shipment expectations revised upward from over 10,000 units for the full year 2026 to over 50,000 units for the 2026-2027 period. This signal demonstrates that NVIDIA is shifting from a pure GPU vendor to a provider of holistic AI compute networking solutions, with its networking business emerging as a critical pillar for long-term growth.

Risk Factors

In examining NVIDIA's growth prospects, it is also necessary to carefully assess potential risk factors.

On the competitive front, Alphabet's positioning in the custom acceleration chip space represents the most direct challenge. As its TPU architecture continues to iterate, coupled with deepening synergy with Broadcom in design and manufacturing, there is a possibility that some general-purpose computing demand could be replaced.

Simultaneously, Amazon has gradually integrated its self-developed Trainium chips into AWS internal workloads. While this reflects robust demand for AI infrastructure among cloud providers in the short term, in the long run, if customers pivot toward more cost-effective in-house solutions, it may exert marginal pressure on NVIDIA's market share.

Notably, the large-model startup Anthropic has significantly increased its procurement ratio of TPUs and Trainium chips, a move that may signal the accelerating implementation of customer diversification strategies.

Deeper concerns focus on the sustainability of capital expenditures; whether the massive investments by hyperscale cloud providers can translate into matching commercial returns remains a core market concern. If the monetization pace of end-user applications lags behind the speed of infrastructure expansion, or if the macro environment leads to downward revisions in growth expectations for cloud providers, investors may reassess the durability of this AI investment cycle, potentially triggering a recalibration of valuation frameworks.

Furthermore, controversy surrounding "circular AI deals" persists in the market. Such arrangements refer to capital and business closed-loops formed among cloud service providers, model developers, and compute power providers, strengthening ecosystem ties through mechanisms such as mutual shareholding and preferential procurement.

NVIDIA has participated in several such collaborations, including strategic investments in companies like OpenAI and CoreWeave. Superficially, this model helps stabilize order visibility and supports valuation logic; however, critics worry that if genuine end-user demand fails to keep pace, the circulation of capital within the system could amplify cyclical volatility.