Intensifying Competition in Large Model Computing Power: In-depth Analysis and Investment Opportunities in US Stock AI Computing Power Market for 2026
With the rapid development of artificial intelligence technology, the scale and complexity of large language models (LLMs) are growing exponentially, driving computing power demand to unprecedented heights. By 2026, this computing power competition has entered a white-hot stage, with the US stock AI computing power market undergoing profound changes, bringing new opportunities and challenges for investors. This article will provide an in-depth analysis of the current large model computing power demand landscape, strategic layouts of tech giants, and industry chain investment opportunities, helping investors grasp the investment pulse in this field.
Analysis of Computing Power Demand Driven by Large Model Development
In 2026, large models have shifted from simple parameter size competitions to in-depth development of practical application scenarios. Models like OpenAI's GPT-5, Google's Gemini Ultra, and Meta's Llama 3.1 have not only broken historical records in parameter count but also demonstrated strong potential in multimodal processing, reasoning capabilities, and vertical domain applications. The training and inference requirements of these large models for computing power show several distinct characteristics:
- Training computing power demand grows exponentially: Industry data shows that in 2026, large model training computing power demand increased by nearly 300% compared to 2025, with expected growth rates exceeding 200% in 2027.
- Inference computing power demand takes over: As large model applications become more widespread, inference computing power demand has surpassed training computing power, becoming the main driver of market growth.
- Specialized chip demand surges: General-purpose GPUs can no longer meet the needs of efficient large model operation, making specialized AI chips like Nvidia's Blackwell architecture, AMD's MI300 series, and Google's TPU v5e market focal points.
Morgan Stanley's latest research report indicates that the global AI computing power market is expected to reach $500 billion in 2026, with large model-related computing power accounting for over 60% of this. This growth is primarily driven by three factors: continuous expansion of large model parameter sizes, diversification of application scenarios, and rapid increase in enterprise AI adoption rates.
Latest Developments in US Stock AI Computing Power Market
In the third quarter of 2026, the US stock AI computing power market presented several key dynamics. First, although Nvidia's Blackwell Ultra (B300) chips face short-term capacity constraints, their significant performance advantages have led to orders extending into the first quarter of 2027. Second, competitors like AMD and Broadcom are accelerating the launch of next-generation AI chips, attempting to break Nvidia's market monopoly. Finally, the AI computing power leasing market is experiencing explosive growth, with several specialized computing power leasing companies like CoreWeave and Nscale securing huge funding, with valuations exceeding $10 billion.
In terms of market performance, AI computing power-related stocks showed strong overall performance in the third quarter of 2026. Nvidia's stock price rose 35% from the beginning of the year, AMD rose 28%, and Broadcom rose 22%. In contrast, traditional semiconductor stocks had an average increase of only 12%, highlighting the relative advantage of the AI computing power sector. However, analysts warn that as more players enter the market, competition will intensify, and valuations of some companies may face correction pressure.
Computing Power Layouts and Strategies of Major Tech Giants
In the AI computing power arms race, several tech giants have shown different strategic orientations:
Nvidia: Building a Computing Power Ecosystem as the GPU Dominator
As the absolute leader in the AI chip market, Nvidia continued to strengthen its advantageous position in 2026. In addition to launching Blackwell Ultra chips, Nvidia has built a strong software moat through the CUDA ecosystem, making it difficult for developers to switch to other platforms. The company recently announced cooperation with multiple cloud service providers to jointly develop AI training solutions based on DGX supercomputers, further reinforcing its position in the high-end training market.
Nvidia CEO Jensen Huang stated in the recent earnings conference: "We not only provide hardware but build a complete AI ecosystem. From chips to software, from training to inference, Nvidia is committed to providing comprehensive support for AI developers." This ecosystem strategy enables Nvidia to maintain its leading position in the AI computing power market.
Microsoft and Google: Emphasizing Both Cloud Services and Self-developed Chips
As leaders in the AI application field, Microsoft and Google have adopted different computing power strategies. Microsoft continues to expand the scale of its Azure AI cloud services while increasing investment in self-developed chips. In the third quarter of 2026, Microsoft announced the launch of its next-generation AI chip Maia 2, specifically optimized for large model inference, which is expected to significantly reduce AI inference costs for its Azure cloud services.
Google adheres to its self-developed chip strategy, with its TPU v5e chips showing strong performance in training large models. Google CEO Sundar Pichai stated in the recent investor conference: "Through self-developed chips, we can better control the performance and cost of AI infrastructure, which is key to maintaining our competitive advantage in the AI field." Additionally, Google announced plans to expand its global data center network to meet growing AI computing power demands.
Meta: Parallel Open Source Strategy and Custom Chips
Meta has adopted a relatively open strategy, attracting numerous developers and enterprises to join its ecosystem by open-sourcing its LLaMA large model. Meanwhile, Meta is accelerating its custom chip projects, including MTIA (Machine Learning Inference Accelerator) and next-generation GPUs, aiming to reduce costs for its large-scale AI training and inference. Meta's AI infrastructure head stated in a recent blog: "The combination of open source and custom chips allows us to provide more powerful AI services at lower costs while maintaining technological leadership."
Analysis of Investment Opportunities in the Computing Power Industry Chain
With the surge in large model computing power demand,各个环节 of the AI computing power industry chain present rich investment opportunities. According to Goldman Sachs' analysis report, the following sub-sectors have the greatest growth potential in the next 12-18 months:
AI Chip Design and Manufacturing
Traditional chip giants like Nvidia, AMD, and Broadcom continue to lead, but startups such as Cerebras, SambaNova, and Groq are also making breakthroughs in specific areas. Companies focusing on inference chips are particularly favored by investors due to their rapidly growing market demand. Additionally, in the chip manufacturing segment, technical breakthroughs by foundries like TSMC and Samsung in 3nm and below advanced processes provide critical support for AI chip performance improvement.
AI Servers and Data Centers
With the surge in large model computing power demand, the AI server market is experiencing explosive growth. Traditional server manufacturers like Dell, HPE, and Lenovo are accelerating the launch of servers specifically optimized for AI. Meanwhile, companies focused on AI servers like Supermicro have shown particularly outstanding performance, with their stock price rising 45% in the third quarter of 2026. Additionally, liquid cooling technology has become a key direction for data center upgrades due to its effectiveness in solving the high power consumption issues of AI servers, with related companies like Vertiv and Asetek gaining market attention.
Computing Power Leasing and Hosting Services
The computing power leasing market has become one of the most dynamic sectors in the AI industry chain in 2026. Professional computing power leasing companies like CoreWeave and Nscale are favored by enterprises and developers due to their flexible computing power delivery models. These companies not only provide computing resources but also offer supporting software and tools, significantly lowering the threshold for enterprises to use large models. According to Bloomberg, the global computing power leasing market reached $15 billion in the third quarter of 2026, a year-on-year increase of 220%.
AI Software and Tools
With the popularization of large model applications, the AI software and tools market is also showing rapid growth. From model optimization tools and distributed training frameworks to AI application development platforms, various software products are emerging. NVIDIA's NeMo, Hugging Face's Transformers library, and open-source DeepSpeed have become developers' first choices. Additionally, model-as-a-service (MaaS) platforms like Anthropic's Claude and Cohere's Command have gained numerous enterprise customers, promoting the popularization of AI applications.
Risks and Challenges
Despite the broad prospects of the large model computing power market, investors should also pay attention to the following risk factors:
- Technology iteration risk: AI chip technology iteration is extremely fast, and current leading companies may soon be disrupted by new technologies. For example, emerging technologies like photonic computing and quantum computing may fundamentally change the computing power supply landscape.
- Capacity bottlenecks: Limited advanced chip manufacturing capacity may lead to supply shortages, affecting the performance of related companies. The delayed shipment of Nvidia's Blackwell Ultra chips is a typical case.
- Regulatory risks: With the widespread application of AI technology, governments may strengthen regulation of AI chips and computing power, restricting the export of key technologies or requiring localized production, which could affect the global AI computing power supply chain.
- Intensifying competition: As more players enter the AI computing power market, competition will become increasingly fierce, potentially leading to declining profit margins in the industry. Especially in the general-purpose GPU market, competitors like AMD and Intel are accelerating their catch-up.
Future Outlook and Investment Recommendations
Looking ahead, the large model computing power market will continue to maintain high-speed growth. According to McKinsey's forecast, by 2030, the global AI computing power market will reach $1.5 trillion, with a compound annual growth rate of about 35%. Against this backdrop, investors can focus on the following types of investment opportunities:
Core Hardware Suppliers
Nvidia, as the leader in the AI chip field, remains the most certain investment target with its strong ecosystem advantages and continuous technological innovation. Additionally, breakthroughs by competitors like AMD and Broadcom in specific areas are also worth noting. Especially those companies that can provide differentiated products, such as chip design companies focusing on inference chips or specific AI application scenarios, may achieve higher growth rates.
Computing Power Infrastructure Providers
With the growth of AI computing power demand, data centers, servers, cooling equipment, and other infrastructure providers will benefit. Especially those companies that can provide high-performance, low-power solutions, such as liquid cooling technology providers and high-density server manufacturers, will gain advantages in competition.
Vertical Industry Solutions
The application of large models in vertical industries such as healthcare, finance, and manufacturing will create numerous investment opportunities. Companies that can combine AI computing power with industry expertise to provide industry-specific solutions will gain higher market recognition and customer stickiness.
Emerging Technology Fields
Emerging technologies like photonic computing and in-memory computing may fundamentally change the computing power supply landscape. Paying attention to technological breakthroughs and commercialization processes in these fields may bring excess returns. Especially those technologies that have been commercialized or are about to be commercialized have higher investment certainty.
Conclusion
The large model computing power competition has become the most eye-catching battlefield in the technology field in 2026. With the popularization and deepening of AI applications, computing power demand will continue to grow, bringing huge development opportunities for the related industry chain. However, investors also need to be alert to challenges such as technology iteration, capacity bottlenecks, regulatory risks, and intensifying competition. In terms of investment strategy, it is recommended to focus on leading companies with core technological advantages, strong ecosystem building capabilities, and clear business models, while also paying attention to investment opportunities in vertical industry solutions and emerging technology fields.
As Morgan Stanley analysts stated in their latest report: "The AI computing power competition is not just a hardware contest, but a comprehensive competition of ecosystems, applications, and business models. Investors need to look beyond pure technical parameters and evaluate companies' long-term competitiveness and investment value from a more comprehensive perspective." In this era of AI transformation, only those companies that can continuously innovate, build complete ecosystems, and meet real market demands can stand out in this computing power arms race and bring generous returns to investors.
