With its frenzied buying spree, where does NVIDIA's ambition lie?
Driven by the global wave of hundreds of billions of dollars in computing power investment, the global artificial intelligence industry is undergoing a profound structural transformation. In the past, the focus of industry competition was highly concentrated on ultra-large-scale data center clusters, where all players competed on the number of GPUs and rushed to build larger model training infrastructures. But by 2026, the industrial logic has shifted significantly: while leading giants continue to increase investment in cloud computing power infrastructure, they are accelerating their deployment to AI terminals and edge scenarios; enterprises engaged in data center infrastructure are seizing the terminal-side market downward, while manufacturers that originally focused on terminal hardware are laying out cloud-side computing power upward. The two-way penetration between terminal and cloud has become a common trend in the industry.
NVIDIA recently officially announced the acquisition of open-source AI platform Hugging Face for 12.9303 billion US dollars, which is also the second largest acquisition in its history. Together with a series of previous moves including customized technology infrastructure, industrial chain financing binding, and AI terminal layout, it is more than enough to show that NVIDIA is no longer satisfied with simply playing the role of a GPU hardware supplier. Relying on the output of technical standards, open customization capabilities, capital ties, developer ecosystem mergers and acquisitions, and terminal deployment, it is extending its ecological reach to the entire end-edge-cloud chain. The underlying motivation behind this series of actions is precisely the industrial traction logic spawned under the pressure of huge investment in the AI industry.
01 From standardized supply to open technology infrastructure, securing the fundamental market of the industry
NVIDIA's expansion path is first reflected in the subversive reconstruction of the customized ecological model. For a long time, the market's perception of NVIDIA has been limited to standardized GPU products, with mass supply of general-purpose chips based on the Blackwell and Vera Rubin architectures, mainly serving cloud vendors and large model enterprises. However, as Google, Amazon, and various supercomputing enterprises have successively developed their own dedicated XPU and ASIC chips, the demand for customized chips from leading customers has risen rapidly, bringing variables to the industry landscape. In order to adapt to their own exclusive model architectures and differentiated business scenarios, cloud vendors and large model enterprises urgently need customized hardware to optimize computing power efficiency, so as to reduce training and inference costs and improve proprietary business performance. This was once regarded as the biggest potential challenge to NVIDIA's traditional general-purpose GPU ecosystem. The industry generally predicts that a large number of core customers turning to self-developed customized chips will gradually break away from the CUDA ecosystem and continuously divert NVIDIA's core market space.
Facing industry changes, NVIDIA has not adopted a conservative strategy of sticking to standardized products and resisting customers' self-development. Instead, it followed the trend and incorporated customization into its core ecosystem, realizing the transformation from "selling hardware" to "outputting technology infrastructure". The launch of the NVLink Fusion platform is a key implementation of this strategic idea. This technical framework integrates market-verified underlying core technical modules such as NVLink high-speed interconnection, NVLink-C2C cross-chip interconnection, and NVHBM high-speed memory, allowing all partners to independently develop customized XPU chips based on this mature infrastructure. The more core advantage is that the customized chips developed by partners can be directly connected to NVIDIA's rack-level AI system, seamlessly adapting to its complete software ecosystem without rebuilding the entire adaptation system.
NVIDIA recently invested 3.5 billion US dollars in MediaTek, and the deep strategic cooperation reached between the two sides is a typical implementation case of this customized model. As the world's leading end-side chip manufacturer, MediaTek can rely on the NVLink Fusion platform to develop exclusive customized AI processors for the differentiated needs of cloud service providers and large model enterprises, helping customers avoid the technical barriers, adaptation problems and high costs brought by zero-based R&D, while allowing customized hardware to always integrate into NVIDIA's AI factory system, without having to start from scratch and break away from the mainstream ecosystem. This model reverses the confrontational relationship between customized chips and NVIDIA's ecosystem, turning potential market competitors into deeply bound ecological collaborators.
The value of the customization strategy has achieved full coverage of the cloud and terminals, and is not limited to the data center track. NVIDIA and MediaTek have iteratively launched the RTX Spark and DGX Spark series chips, which are adapted to terminal scenarios such as AI PCs, developer workstations, and enterprise local computing devices, realizing the deep integration of GPU computing power and SoC system capabilities, and providing exclusive customized hybrid computing solutions for various terminal devices. For industry customers, the open customization model greatly reduces the technical threshold and R&D cost of self-developed chips, without the need to invest huge sums of money to build a complete set of software and hardware technology stacks; for NVIDIA, it has successfully secured the fundamental market of the full-domain ecosystem. Even if customers choose to develop customized hardware by themselves, they will always stay within the interconnection, memory and software systems built by it, realizing ecological expansion and continuous control.
This acquisition of Hugging Face further complements the upper software link of the customized ecosystem. In the future, various customized chips produced by NVLink Fusion can be directly connected to Hugging Face's massive model library, developers can complete model adaptation and tuning on the platform, and the deployment threshold of customized hardware is further reduced. Even if customers develop chips by themselves, as long as the workflow of model development, fine-tuning and deployment takes place on the Hugging Face platform, it will be indirectly incorporated into NVIDIA's ecological track, hedging the risk of "de-NVIDIAization".
02 Taking capital as the link to solve the high-investment industrial dilemma of AI
If customization is NVIDIA's ecological expansion means at the technical level, then financing empowerment and industrial capital binding are its core starting points for expanding against the trend and leading industry development.
The capital expenditure of the current AI industry has entered an unprecedentedly high range. Many authoritative institutions estimate that the cumulative investment in global AI infrastructure in the coming years will reach the trillion-dollar level. The construction of a modern ultra-large-scale AI computing power factory covers multiple links such as chip procurement, computer room construction, power supply and heat dissipation, network deployment, and technical operation and maintenance. The investment of a single project often amounts to tens of billions of dollars. Relying solely on the cash flow of large model enterprises and computing power manufacturers, it is impossible to continuously cover the huge investment. The high cost threshold has become the core bottleneck restricting the large-scale deployment of the AI industry.
Against the background of huge industry investment and pressure on individual enterprises, a new cycle model of "capital driving industry, industry feeding back capital" came into being. Leading enterprises with NVIDIA as the core, relying on abundant operating cash flow, deeply bind upstream and downstream industrial chain customers through various methods such as equity investment, convertible bond investment, joint project financing, and industrial fund linkage. Downstream enterprises obtain financial support to afford the high cost of computing power construction and technology R&D; upstream hardware manufacturers lock in long-term and stable order demand, forming a closed-loop business cycle, strongly driving the large-scale deployment of AI infrastructure, and solving the development dilemma of the industry with high investment and slow return.
NVIDIA, which holds sufficient cash flow, has elevated industrial strategic investment to the core strategic level. Since 2026, the scale of its equity investment commitments has continued to rise, and the investment targets fully cover the entire AI industrial chain including large model R&D enterprises, computing power operators, high-end chip design, optical interconnection hardware, and AI terminal hardware. Different from traditional financial investment that pursues short-term returns, all of NVIDIA's investment actions are centered on industrial collaboration, with extremely strong strategic pertinence. It not only directly invests heavily in leading global large model companies such as OpenAI and Anthropic to bind top AI R&D resources, but also joins many of the world's top asset management institutions to leverage hundreds of billions of dollars in third-party social capital, which is specially used for the construction of global AI computing power cluster projects.
Among them, the hundred-billion-level AI infrastructure cooperation reached with OpenAI is the most representative. While providing hardware computing power support, NVIDIA simultaneously supports large-scale capital investment, and releases funds in batches according to the progress of computing power deployment to ensure the continuous advancement of the project. At the same time, each of its investments in computing power infrastructure operators, high-speed optical device manufacturers, and semiconductor design enterprises corresponds to a clear hardware procurement and project deployment agreement, realizing deep linkage and two-way empowerment between capital investment and industrial deployment.
In the same week when the acquisition of Hugging Face was officially announced, the market spread the news that NVIDIA is negotiating to invest in AI search enterprise Perplexity, and the enterprise's valuation is expected to exceed 30 billion US dollars after the transaction is completed. Combined with previous large investments in Intel, Synopsys, Coherent, Corning, MediaTek, and participation in financing of enterprises such as OpenAI, Anthropic, SpaceX, an industrial network covering chip manufacturing, hardware supply chain, model distribution, and AI applications is taking shape.
This financing binding model reconstructs the business cooperation logic of the AI industry, breaking out of the traditional supply and demand buying and selling relationship. Through equity nesting and capital binding, NVIDIA firmly binds the interests of chip suppliers, computing power builders, large model R&D enterprises, and terminal application manufacturers into a community. Large model enterprises obtain low-cost industrial funds to ease the pressure of huge R&D and computing power investment; NVIDIA locks in long-term deterministic hardware procurement demand, continuously digests chip production capacity and consolidates market share; computing power operators obtain stable project orders, continuously expand the scale of infrastructure and improve service capabilities. Capital has become the core link connecting the entire industrial chain, effectively solving the practical problem that the AI industry has too much investment for a single enterprise to bear independently, and accelerating industrial iteration and upgrading with the power of capital.
However, the potential risks of this model cannot be ignored. This kind of industrial expansion highly dependent on capital-driven has obvious leverage attributes. Once the commercial deployment speed of AI is lower than expected and the industry's profit return lags behind, after the capital recedes, the entire nested industrial chain will face the risk of cascading pressure, which is also a systematic industry risk that authoritative institutions such as the Bank for International Settlements have focused on warning. The acquisition of Hugging Face itself is also a high-premium strategic investment. The platform's annual revenue is only about 150 million US dollars, and the price-to-sales ratio of the acquisition reaches 86 times. The return does not come from short-term financial returns, but to obtain the developer entry. Once the demand for AI applications is lower than expected, this huge acquisition will also bring asset-level pressure.
03 Grasp the two-way migration of end and cloud to seize new industrial increments
In addition to building a customized ecosystem and deep capital binding, fully entering the AI terminal market to achieve full-domain coverage of end-edge-cloud is NVIDIA's third core strategy to stabilize its dominant position in the industry and tap new increments.
At present, a significant two-way migration trend is taking place in the global AI industry, and the industry pattern has bid farewell to the era of single cloud computing power. Traditional cloud vendors and computing power enterprises that focus on data centers are penetrating downward into terminals and edge scenarios; terminal manufacturers that are originally rooted in consumer electronics, automotive, and industrial hardware are continuously laying out cloud data center computing power business upward. Two-way penetration, integration and collaboration between end and cloud have become a deterministic trend in the industry.
The core logic of this industrial transformation stems from the iterative upgrading of the computing power architecture. Ultra-large-scale cloud computing power clusters still undertake the core tasks of large-parameter model training and massive data processing, but scenarios such as model inference, lightweight computing, and real-time interaction, driven by three core factors of latency, privacy and cost, are rapidly sinking from the cloud to edge devices and terminal hardware. According to public data from IDC, the global shipment of AI PCs will exceed 50 million units in 2026, and the shipment of AI mobile phones is expected to exceed 430 million units. The growth rate of the end-side AI chip market has continued to be significantly higher than that of cloud chips. The terminal market has evolved from an auxiliary link of the AI industry to a core incremental track for industry growth.
For a long time, NVIDIA's core main battlefield has been concentrated in the data center server track, and its Jetson series edge products are relatively small in size and its terminal layout is relatively limited. In the past two years, NVIDIA has fully adjusted its strategy, accelerated the penetration of all categories of AI terminals, and made up for its industrial shortcomings. In the consumer terminal field, NVIDIA has joined hands with global hardware partners to create cost-effective AI PC chip solutions, continuously sinking high-end RTX computing power capabilities to personal terminal devices, allowing ordinary users' local devices to independently run lightweight AI tasks such as generative AI, intelligent drawing, and local Q&A, getting rid of dependence on cloud servers.
In the field of in-vehicle intelligent terminals, NVIDIA and MediaTek jointly build a software-defined automotive AI platform, focusing on core scenarios such as in-vehicle physical AI computing, intelligent driving, and in-cabin interaction, laying out core in-vehicle computing power to seize the AI track of intelligent vehicles. In the field of industry and robotics, it continues to iterate and upgrade the Jetson series of edge hardware products, providing highly adapted, low-power end-side computing power modules for scenarios such as industrial intelligent manufacturing, humanoid robots, and IoT edge nodes, empowering industrial intelligent upgrading. At the same time, its recent intensive acquisition and investment actions are all centered on the terminal ecosystem. By investing in AI application development, terminal chip design, and edge algorithm enterprises, it makes up for the shortcomings of end-side software and hardware capabilities, and connects the complete computing power link from the cloud AI super factory to personal terminals, intelligent vehicles, and industrial robots.
The addition of Hugging Face further complements the software shortcomings of end-side AI. The massive lightweight models and datasets on the platform can be directly supplied to AI PCs, robots, and in-vehicle terminals for local fine-tuning and deployment. Developers can complete the entire process of cloud model training, lightweight pruning, and end-side deployment in one stop, opening up the model circulation channel between cloud and end. In the past, NVIDIA only had end-side hardware, and model resources were in the hands of third parties; after the acquisition, the hardware and open-source model library form a collaboration, providing a software infrastructure for end-cloud collaboration.
NVIDIA's terminal layout is essentially a prediction of industry trends. At the moment when end-cloud collaboration has become the final architecture of AI, simply controlling the cloud computing power infrastructure can no longer support long-term development. Only by mastering both large cloud training computing power and small terminal inference computing power, covering all end-edge-cloud scenarios, can we occupy an absolute dominant position in future industry competition. NVIDIA actively sinks into the terminal market, precisely to break out of the limitation of the single cloud track, seize new industrial increments, and build a full-domain computing power ecological barrier.
Looking at NVIDIA's three mutually supporting and complementary strategies of customized ecology, financing capital binding, and full-domain AI terminal layout, they jointly build NVIDIA's unshakable industrial moat, which is not only a strategic choice to seize the dividends of the times, but also its core confidence to cope with future industrial changes and continue to lead the AI track.
This article is from the WeChat official account "Semiconductor Industry Insight" (ID: ICViews), the author is Zi Hao, published with authorization from 36Kr.