AI Chip Market Analysis

Processing Type Segment Analysis

In the artificial intelligence (AI) chip market, the edge subtype based on processing type is expected to attain the largest revenue share of 75.6% over the forecasted years. The heightened demand for real-time processing in IoT, autonomous systems, and industrial automation is the key factor solidifying the segment’s dominance. Simultaneously, edge deployment reduces latency and increases energy efficiency, driving sustained market growth. In November 2025, Kneron announced the launch of its KL1140 NPU chip, which is the first to run full Mamba networks on-device that enabling powerful LLMs at the edge with up to 3 times better energy efficiency and 10 times lower cost when compared to cloud-based AI. This KL1140 allows real-time, secure AI applications such as robotics, automotive systems, and private enterprise assistants without any reliance on cloud connectivity, reducing latency and energy use, hence denoting a wider segment scope.

Chip Type Segment Analysis

By the conclusion of 2035, the CPU based on chip type will grow at a considerable rate in the artificial intelligence chip market due to its versatility in handling diverse AI workloads, supporting both training and inference tasks across cloud and edge platforms. Also, their compatibility and widespread deployment make them a top revenue contributor in this sector. In September 2025, NVIDIA and Intel announced that they had entered into a strategic collaboration to develop next-generation AI infrastructure and personal computing products, tightly integrating NVIDIA’s AI and accelerated computing with Intel’s x86 CPU technologies using NVIDIA NVLink. In this context, Intel will design custom x86 CPUs for NVIDIA’s data center AI platforms and new x86 SoCs with NVIDIA RTX GPU chiplets for high-performance PCs, targeting hyperscale, enterprise, and consumer markets. Furthermore, as part of the deal, NVIDIA will invest USD 5 billion in Intel’s common stock, underscoring a long-term partnership to shape the future of AI-based computing.

Technology Segment AnalysisBottom of Form

In the technology segment, machine learning is expected to grow with a significant share in the AI chip market over the discussed time frame. The wide adoption in applications such as NLP, computer vision, and predictive analytics is the key factor behind this leadership. ML workloads require advanced compute and specialized AI chips, driving continued revenue growth in the sector. In addition, the rising deployment rates of AI at the edge, expansion of AI data centers, and growing demand for real-time inference are accelerating chip innovation, whereas increased investments in custom AI processors and heterogeneous architectures are efficiently strengthening market momentum. Moreover, the existence of supportive software ecosystems and frameworks is also improving the accessibility and scalability of ML solutions. Furthermore, these trends reinforce machine learning’s significant role in shaping the future of the AI chip market.

Our in-depth analysis of the artificial intelligence (AI) chip market includes the following segments: