Nvidia management explains: Where does the market-shaking 70% revenue guidance come from?

Nvidia's unusual move to provide a multi-year revenue outlook has drawn widespread attention.

In its latest report released on September 2nd, JPMorgan Chase stated that, based on recent discussions with Toshiya Hari, NVIDIA's Vice President of Investor Relations and Strategic Finance, this 70% year-over-year growth framework is not driven by a single factor, but rather built upon a comprehensive acceleration of demand from hyperscale cloud vendors, emerging cloud service providers, AI labs, sovereign AI, and enterprise clients. More importantly, management explicitly stated that without supply constraints, business growth could even double—the core variable currently limiting growth is supply, not demand.

Nvidia's proactive disclosure of its forward-looking guidance for fiscal year 2028 stems directly from the need to bridge the significant gap between market consensus and the company's internal assessment. Management believes that allowing this gap to persist could pose substantial challenges to the planning of its supply chain partners. This statement implies that the 70% growth target is seen by management as a well-supported "comfort zone," rather than an aggressive forecast , and its implied upside potential far exceeds current market pricing.

Meanwhile, Nvidia has released important signals across multiple dimensions, including customer structure, inference business share, supply bottlenecks, and financing arrangements, further outlining the medium-term growth prospects of this AI chip giant. JPMorgan Chase maintains its overweight rating on Nvidia with a target price of $320, implying approximately 43% upside from the current share price of $224.41 (closing price on September 2).

Supply, not demand, is the real ceiling for growth.

The report states that JPMorgan Chase found in a conversation with Toshiya Hari, Nvidia's Vice President of Investor Relations and Strategic Finance, that Nvidia's management showed clear confidence in the 70% growth framework, but also defined its boundaries: it is a supply-constrained figure, not a demand-constrained one.

According to a JPMorgan report, Toshiya Hari pointed out in a discussion that if supply constraints were excluded, Nvidia's business growth could more than double year-on-year. This statement directly reveals the conservative nature of the current guidance— 70% is based on expectations under actual supply conditions, rather than the upper limit of demand that the company can reach.

Management also specifically stated that the decision to provide multi-year outlook stemmed in part from a "meaningful gap" between market consensus and internal company observations. Without proactive disclosure, this information asymmetry could lead to mismatches in capacity planning among supply chain partners, which in turn could constrain Nvidia's own delivery capabilities. In other words, this forward-looking guidance is both a signal to investors and a proactive management strategy for the supply chain ecosystem.

Advanced wafers and memory: two core bottlenecks in the supply chain

Regarding the specific composition of supply constraints, Toshiya Hari specifically named two of the most critical materials: advanced wafers and memory , listing them as the two items with the highest weight in Nvidia's Bill of Materials (BOM).

According to a report by JPMorgan Chase, Nvidia is currently in deep communication with TSMC and three memory suppliers—Micron, SK Hynix, and Samsung—with the core issues revolving around improving supply availability.

The bank believes that Toshiya Hari's statement indicates that Nvidia's supply chain management has entered a phase of intensive proactive coordination, rather than passively waiting for capacity release. The pace of easing these bottlenecks will directly determine whether Nvidia can achieve growth exceeding the 70% benchmark in FY28. The potential for improvement in the supply chain translates into potential for performance flexibility.

The proportion of inference business continues to expand, but the platform's versatility makes quantitative analysis difficult.

The revenue structure of inference and training has been a long-standing topic of market attention. In the exchange, Toshiya Hari gave the clearest directional judgment to date: about 18 months ago, the revenue share of training and inference was roughly equal; but now inference has surpassed training, and this trend is expected to continue.

However, the report also noted that management pointed out the inherent difficulty in accurately quantifying the separation ratio between the two. This is due to the high fungibility of NVIDIA's platforms—for example, customers can use the Grace Blackwell product for training workloads and then switch the same hardware assets to inference tasks. This flexibility is a key competitive advantage of NVIDIA's platforms, but it also limits external analysis of the revenue structure.

The customer base continues to diversify, with emerging cloud vendors contributing over 50%.

Nvidia's revenue streams are expanding from hyperscale cloud providers to a broader ecosystem. According to Toshiya Hari, OpenAI and Anthropic, two leading cutting-edge model builders, currently account for about 20% of Nvidia's business in the consumer segment, and this proportion is expected to rise to about 25% in FY28.

It is worth noting that the figures above reflect the proportion at the end-consumer level, rather than Nvidia's direct customer structure—Nvidia typically sells computing power to hyperscale cloud vendors or emerging cloud service providers, who then sell computing capacity to model builders.

At the direct customer level, the contribution of emerging cloud service providers (neoclouds) is already significant. According to a JPMorgan report, emerging cloud service providers currently account for more than 50% of NVIDIA's ACIE (Accelerated Computing and AI Infrastructure Ecosystem) business , indicating that NVIDIA's growth engine no longer relies solely on a few leading cloud vendors, but is driven by a broader ecosystem of computing power construction and leasing.

The open-source vs. closed-source debate: Nvidia's answer is "both are necessary".

In the market debate surrounding the merits of open-source versus closed-source Large Language Models (LLMs), NVIDIA management has given a clear stance: the two are not mutually exclusive competitors, and the continued evolution of AI requires the collaborative development of both open-source and closed-source models.

Toshiya Hari stated that NVIDIA widely uses closed-source models such as OpenAI and Claude internally, while in mission-critical scenarios such as chip design, a combination of closed-source and open-source approaches is used. The core logic of management is that as long as model builders can commercialize the technology and continuously improve the economic model, demand will continue to be transmitted to chip suppliers such as NVIDIA.

Furthermore, management mentioned that the gross margin (GM) for model builders appears to be improving, partly due to the decreasing cost per token per generation driven by the NVIDIA platform. This improvement in model economics creates a positive feedback loop for demand for NVIDIA chips.

The financing arrangement aims to support forward demand; management refutes allegations of "revolving financing."

Nvidia's recent financing arrangements have attracted market attention, and management provided a systematic explanation during this exchange.

According to a JPMorgan report, the arrangements include: revenue-sharing agreements with some emerging cloud service providers, the PORTS-Pike data center campus plan, and a $500 billion private equity financing platform jointly established with institutions such as Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR.

Regarding revenue sharing agreements, Nvidia's mechanism is to set a base price floor for computing power leasing and share the upside when the market leasing price is higher than the benchmark , thereby creating a recurring revenue option in addition to core hardware sales.

In response to concerns about "revolving financing," management clarified that the financing arrangements are of moderate size, have upper limits, and are supported by strong underlying demand, ecosystem returns, and the creditworthiness of the ultimate computing power buyers. Nvidia positions these financing tools as a means to support forward-looking AI infrastructure needs, rather than as financial leverage.

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