NVIDIA GPU Monopoly Faces Challenges: AMD and Intel Accelerate Breakthrough, AI Computing Market Sees Changes
In September 2026, the global AI computing power market is undergoing a profound transformation. As the long-time dominant player in AI computing, NVIDIA's GPU monopoly position is facing strong challenges from AMD and Intel. With the continuous expansion of AI application scenarios and the explosive growth of computing power demand, the competitive landscape among the three major chip giants is being reshaped, bringing new opportunities and challenges for investors.
NVIDIA GPU: Unshakeable Market Leader
NVIDIA has established an unshakeable leading position in the AI computing field through its CUDA ecosystem and Blackwell architecture GPUs. According to the latest financial report data, NVIDIA's data center business reached a record $18.5 billion in revenue in the second fiscal quarter of 2026, a year-on-year increase of 68%, accounting for 78% of the company's total revenue. This performance not only demonstrates NVIDIA's dominance in the AI GPU market but also reflects the explosive growth of global AI computing power demand.
The Blackwell architecture GPU, as NVIDIA's latest flagship product, uses TSMC's 4NP process technology, integrates more than 200 billion transistors, and provides up to 20 petaflops of AI performance. Compared to the previous Hopper architecture, Blackwell has improved energy efficiency by 3 times while supporting more advanced sparse computing technologies optimized for AI workloads.
However, NVIDIA's success is not solely dependent on hardware advantages. Its CUDA ecosystem has become the de facto standard for AI development, with over 4 million developers worldwide relying on this platform for AI model training and inference. This ecosystem advantage creates a strong network effect, making it difficult for competitors to replicate in the short term.
AMD Accelerates Catch-Up: MI400 Series Chips Make a Strong Entry
Facing NVIDIA's dominant position, AMD is accelerating its catch-up through its MI400 series AI acceleration chips. According to AMD's latest financial report, its data center and AI business revenue grew by 95% year-on-year in the second quarter of 2026, reaching $3.8 billion, a growth rate significantly higher than the industry average.
The AMD MI400 series is based on the CDNA 4 architecture, uses TSMC's 5nm process technology, delivers 16 petaflops of single-chip computing power, and supports FP8 and INT4 precision computing, making it particularly suitable for large language model training and inference. MI400 offers 3 times the performance of the previous MI300 while reducing power consumption by 30%, showing strong competitiveness in energy efficiency.
AMD's strategic advantage lies in its CPU+GPU heterogeneous computing platform. By combining the MI400 series with EPYC CPUs, AMD can provide complete AI computing solutions to meet various needs from edge computing to data centers. Additionally, AMD is actively expanding its software ecosystem, with the number of developers on the ROCm platform doubling in the past year, narrowing the gap with CUDA.
Notably, AMD is establishing strategic partnerships with multiple cloud computing giants. Amazon AWS, Microsoft Azure, and Google Cloud have all announced plans to adopt MI400 series chips, providing more options for AI workloads. These partnerships will bring stable revenue sources for AMD and accelerate the market penetration of its AI chips.
Intel Refuses to Be Outdone: Gaudi 3 Chips Target AI Inference Market
As a traditional giant in the semiconductor industry, Intel is actively entering the AI computing market through its Gaudi series AI acceleration chips. Intel's newly released Gaudi 3 chip uses Intel 7 process technology, provides up to 2.5 petaflops of BF16 computing power, and is specifically optimized for AI inference workloads.
Intel's strategic focus differs from that of AMD and NVIDIA. The Gaudi series chips emphasize collaborative optimization with Intel Xeon CPUs and simplify AI application development through the oneAPI unified programming model. This integrated solution aims to reduce the complexity of enterprise AI deployment while improving overall system performance.
Intel is also accelerating its technical accumulation in the AI computing field by acquiring AI chip companies like Habana Labs. These acquisitions have not only brought advanced technology but also provided Intel with opportunities for strategic partnerships with cloud computing giants. Market analysts predict that Intel's AI chip business revenue could reach $5 billion in 2026, becoming a new growth engine for the company.
Development Trends and Investment Opportunities in the AI Computing Power Market
As AI applications continue to deepen, the AI computing power market is showing diversified development trends. First, the demand for computing power from large language model training will continue to grow, with global AI training computing power demand expected to increase more than 10 times by 2028. Second, the AI inference market will experience explosive growth, with edge AI and terminal AI applications driving demand for specialized AI chips.
From an investment perspective, the changing competitive landscape among the three major chip giants provides diversified choices for investors. NVIDIA, with its ecosystem advantages, remains the preferred investment target in the AI computing power field; AMD, with its cost-effective products and rapidly growing market share, shows strong investment potential; Intel, through its complete computing platform and strong manufacturing capabilities, has competitive advantages in specific AI application scenarios.
Additionally, companies related to the AI computing power industry chain are also worth attention. This includes AI server manufacturers such as Supermicro and Dell; HBM memory suppliers such as SK Hynix and Micron; and AI software and algorithm companies, all of which will benefit from the continuous growth of AI computing power demand.
Risks and Challenges
Despite the broad prospects of the AI computing power market, investors still need to pay attention to related risks. First, the acceleration of technological iteration may cause existing products to become obsolete quickly as chip architectures and process technologies are rapidly updated. Second, geopolitical factors may affect the stability of the global semiconductor supply chain, especially for advanced process chips. Furthermore, changes in regulatory policies may also have a significant impact on the industry, including export restrictions on AI technology and data privacy regulations.
For investors, it is recommended to adopt a diversified investment strategy, focusing on companies with core technical advantages and strong ecosystems, while paying attention to industry development trends and technological changes to adjust investment portfolios in a timely manner.
Conclusion
NVIDIA's GPU monopoly faces challenges as AMD and Intel accelerate their breakthrough, bringing changes to the AI computing power market. The three major chip giants each have their advantages, and competition will drive technological innovation and market development. For investors, the AI computing power field still offers opportunities, but it is necessary to closely follow industry dynamics, grasp technological trends, and make wise investment decisions.
With the continuous advancement of AI technology and the expansion of application scenarios, the AI computing power market will maintain a long-term growth trend. Whether it's NVIDIA, AMD, or Intel, all will play important roles in this market, jointly promoting the development and popularization of AI technology. For investors, paying attention to these companies' technological innovation, market expansion, and strategic layout will help seize investment opportunities in the AI computing power market.
