英伟达推算力换营收方案 扶持AI初创企业

This article outlines NVIDIA's newly launched partnership program for AI startups and discloses the company's latest fundraising update. Key takeaways are as follows:

1. Core program rules: NVIDIA provides participating AI startups with access to full-stack computing resources powered by its chips, in exchange for a percentage of future revenue from the startups' products and cloud services. All participating companies are required to share revenue with NVIDIA per the agreement.

2. Initial computing capacity: Two founding partners are supporting the program's first batch of capacity. Australia-based Sharon AI will deploy up to 40,000 NVIDIA GPUs, while Singapore's Firmus Technologies is building a 360-megawatt data center in Indonesia that can accommodate up to 170,000 NVIDIA GPUs.

3. Supporting safeguards and fundraising: NVIDIA provides financial guarantees to participating cloud service providers and covers costs for idle GPUs through a buy-back lease arrangement. Additionally, NVIDIA recently announced plans to raise at least $20 billion for corporate operations and refinancing.

NVIDIA's new partnership model offers valuable insights for brand positioning and market expansion in the AI industry. Key takeaways are as follows:

1. Implications for brand strategy: NVIDIA has moved beyond its traditional model of selling chips directly to large enterprise clients. By tying up with a large base of emerging AI startups through a "compute capacity for revenue" model, paired with risk-mitigation measures that lower the barrier to entry for partners, the company effectively diversifies business risk, reduces its reliance on major clients like Amazon and Microsoft, and buffers against competition from large clients' in-house hardware development efforts.

2. Emerging industry collaboration trend: AI startups broadly face challenges of insufficient compute capacity, unstable GPU supply and high costs. A growing number of chipmakers and AI companies have already adopted revenue or equity-sharing partnerships to alleviate these pressures, indicating that this model of upstream-downstream risk-sharing and resource binding will become a new mainstream collaboration trend in the AI sector.

3. Opportunities for emerging market positioning: Brands can adopt this model to proactively partner with emerging AI startups early, capture growth dividends from the fast-expanding AI industry, and unlock new revenue streams.

For AI startup founders and industry practitioners, NVIDIA's new program brings clear opportunities and notable risks. Key takeaways are as follows:

1. Clear startup opportunities: The biggest barrier for AI startups today is limited compute capacity, as upfront GPU procurement requires massive capital investment that leaves many projects stalled. This program eliminates the need for large upfront investment from startups, which gain access to stable compute capacity in exchange for a share of future revenue, significantly lowering the barrier to entry for AI entrepreneurship. Small and medium-sized founders can leverage this opportunity to access critical resources.

2. Risk warning: Participating startups are required to cede a share of revenue to NVIDIA over the long term, which reduces project profit margins. Founders should conduct thorough cost-benefit analysis before entering the partnership to avoid eventual insolvency.

3. Industry trend indication: NVIDIA is currently restructuring its customer base to gradually reduce reliance on large tech giants, and will allocate more resources to small and medium-sized AI projects. This creates a rare window of opportunity for smaller industry players to grow.

For factories engaged in AI hardware manufacturing and data center infrastructure, NVIDIA's new program opens clear business opportunities and strategic insights. Key takeaways are as follows:

1. Immediate near-term business opportunities: Per the program's initial plans, the two founding partners will deploy more than 210,000 NVIDIA GPUs in total and build a 360-megawatt large-scale data center. This creates massive order demand for upstream GPU component manufacturing, infrastructure construction, electrical and mechanical supporting facilities, and other related manufacturing sectors. Relevant factories can proactively pursue supporting contracts for the program to drive new revenue growth.

2. Long-term industry demand trend: The compute capacity shortage is a widespread problem across the AI industry, and more companies will adopt this capacity expansion model. This means demand for GPUs and data center infrastructure will remain at high levels over the long term. Relevant manufacturers can proactively expand targeted production capacity to align with industry growth.

3. Implications for transformation: The rapid expansion of the AI industry is driving digital transformation across all sectors. Related factories can capitalize on this industry boom to advance their own digital and intelligent upgrading, to better meet the customized manufacturing needs of the AI sector.

For AI compute providers, cloud service providers and other related service providers, this article outlines the latest industry trends and actionable solution frameworks. Key takeaways are as follows:

1. Core pain points of current clients: Downstream AI startups and small and medium-sized AI developers broadly face unstable compute supply, high upfront costs, and persistent capacity gaps, which represent a key entry point for service providers looking to expand their business.

2. A replicable collaborative solution: NVIDIA's "compute capacity for revenue" model includes a comprehensive risk-mitigation framework: it provides financial guarantees to participating cloud providers, leases back idle GPUs to cover costs for unused capacity, and meets financing needs for both GPU procurement and data center development. This risk-sharing model effectively removes barriers to capacity expansion for service providers, making it a valuable framework to replicate.

3. New growth opportunities: NVIDIA is restructuring its customer base to reduce reliance on large tech giants and increase resource allocation to small and medium-sized AI projects. Service providers can partner with NVIDIA on this program to expand their customer base and unlock new growth opportunities.

For AI compute platforms and tech marketplaces, NVIDIA's new program offers multiple insights for operation management and ecosystem building. Key takeaways are as follows:

1. Core user demand: The most critical demand for AI industry participants, especially small and medium-sized startups, is stable, low-cost compute access, paired with risk mitigation and financing support. Platform ecosystem building should center its service framework around solving this core pain point.

2. A replicable platform operation model: NVIDIA's revenue-sharing compute model binds partners to the ecosystem through resource exchange for long-term returns, solves the cash flow crunch facing startups, and includes built-in risk mitigation (financial guarantees for partner service providers and leasebacks for idle capacity to reduce partner risk). This risk-sharing, profit-sharing model is ideal for platform ecosystem building, and can be adapted to attract more small and medium-sized operators to the platform.

3. Strategic industry indication: Leading cloud providers are all developing their own in-house AI hardware, while upstream chipmakers are diversifying their customer bases. Platforms can capitalize on this industry restructuring window to step up recruitment efforts in the AI sector, onboard more small and medium-sized AI companies and compute service providers, and完善 their AI ecosystem布局。

For AI industry researchers, this article discloses the latest industry developments and business model innovation in the AI chip sector. Key research takeaways are as follows:

1. A groundbreaking new business model innovation: NVIDIA has broken away from the traditional chipmaker model of generating revenue via direct chip sales, and launched an entirely new "compute capacity for revenue" partnership model. It provides compute capacity to AI startups in exchange for a share of long-term future revenue, and includes supporting financial guarantees to resolve idle capacity risk for partners while meeting financing needs for both GPUs and data centers. This represents a highly innovative business model experiment for upstream players in the AI industry.

2. New dynamics of industrial structural change: NVIDIA's core clients are currently large tech giants including Amazon, Microsoft and Google, all of which are developing in-house AI hardware to reduce reliance on external chip suppliers. NVIDIA's new program is designed to cut its dependence on large anchor clients and expand its small and medium-sized customer base, reflecting the ongoing structural shift in the AI chip industry supply chain.

3. Reflection of common industry-wide challenges: The emergence of this model confirms that AI startups broadly face common challenges including scarce compute capacity, unstable GPU supply, financing difficulties and heavy cash flow pressure. This model offers a new pathway for resolving industry-wide liquidity pressure, making it worthy of in-depth research.