Market Overview
According to the new market research report "China AI Terminal Market Report 2024–2030" published by QYResearch, the China AI terminal market size is projected to reach USD 20.86 billion by 2030, at a CAGR of 22.6% during the forecast period.

AI terminals denote computing devices—principally AI phones, AI PCs, and adjacent smart hardware—that execute artificial intelligence workloads locally through dedicated neural processing units and on-device inference engines, rather than relying exclusively on cloud-based processing. The category's defining characteristic is the migration of inference from data-center to device, which reduces latency, preserves privacy, and enables functionality independent of network availability—attributes that have become central to product differentiation across the Chinese consumer electronics market.

Competitive Landscape
According to QYResearch Top Players Research Center, key manufacturers of AI terminals in China include Lenovo, Huawei, Apple, Honor, vivo, Xiaomi, OPPO, ASUS, HP, and Dell, among others. In 2023, the China top ten players held approximately 84.0% of market share in terms of revenue—a highly consolidated structure reflecting the scale economics of consumer electronics manufacturing, brand equity in distribution channels, and the supply-chain leverage required to secure leading-edge AI chip allocations.

Product Type Segmentation
In terms of product type, AI Phone is currently the largest segment, holding a share of 53.9%. AI phones dominate because smartphones constitute the largest and most frequently replaced consumer computing category in China, and on-device AI features—computational photography, voice assistants, and personalized recommendation—have become mainstream differentiators across mid-range and premium tiers.

Application Segmentation
In terms of product application, Personal Use is currently the largest segment, holding a share of 53.3%. This concentration reflects the consumer-driven character of the Chinese AI terminal market, in which individual purchasing decisions—rather than enterprise procurement cycles—govern volume and upgrade cadence.

Market Drivers
Surge in demand for intelligence: As consumer demand for intelligence, personalization, and convenient living grows, the AI terminal market has experienced explosive growth. Whether AI phones, AI PCs, or other smart devices, AI technology effectively improves interactive experience, efficiency, and functionality—through voice assistants, smart recommendation, and automatic optimization—making these devices essential tools in users' daily lives.

Empowerment of 5G and edge computing: The proliferation of 5G technology and application of edge computing provide AI terminals with more powerful data transmission and real-time processing capabilities. The high-speed, low-latency network enabled by 5G allows AI terminals to achieve smoother cloud interaction and real-time decision-making, while edge computing enables devices to process local data more efficiently, reducing cloud dependence and improving response speed.

Advances in hardware and chip technology: Advancement of AI chips has endowed AI terminals with stronger computing and processing capabilities. Whether for mobile phones, PCs, or other smart hardware, AI chips can perform efficient artificial intelligence operations on-device to improve machine learning, image recognition, and natural language processing outcomes, greatly enhancing AI terminal performance and further stimulating market demand.

Expansion of cross-industry application scenarios: The maturity of AI technology has led to widespread application of AI terminals across industries. From AI phones and AI PCs in the consumer market to smart offices, smart customer service, and automated production in the enterprise field, application scenarios are constantly expanding, driving penetration of AI devices across healthcare, education, retail, and transportation.

Market Restraints
Rapid technology and product iteration: AI technology is developing rapidly, and the speed of technology updates requires AI terminal manufacturers to continuously upgrade hardware and software to keep pace with market demand. This rapid iteration may expose manufacturers to high R&D costs and resource investment, while early-purchased devices may quickly become obsolete, affecting consumer confidence.

Fierce market competition: As an increasing number of technology companies enter the AI terminal market, competition is intensifying. Large companies such as Apple, Google, and Microsoft already occupy substantial market share, while emerging companies face pressure in technology, capital, and brand. As market saturation increases, differentiating through innovation and strengthening brand influence has become a major challenge for manufacturers.

Hardware and software compatibility issues: AI terminal devices depend on robust hardware support and efficient software cooperation. Compatibility issues between different devices and operating systems may degrade user experience—for example, certain AI applications may not be fully compatible across devices, resulting in failure to operate properly or poor performance, which affects market acceptance.

High costs and low profit margins: AI terminals typically require substantial computing power and high-performance hardware support, elevating production costs. For consumers, high prices may become a purchase barrier; for manufacturers, despite increased demand, higher R&D and production costs may compress profit margins and even affect the industry's sustainable development.

Industry Depth: Recent Developments and Technical Challenges
Over the past six months, the China AI terminal sector has seen accelerating integration of large language models into on-device assistants, alongside intensified competition around NPU TOPS ratings and memory capacity as flagship differentiation criteria. A representative development involves Chinese smartphone OEMs partnering with domestic large-model developers to deploy distilled language models on-device, enabling offline summarization and conversational features while addressing data-residency requirements—illustrating how regulatory and privacy considerations are reinforcing the case for local inference over cloud dependence.

Key technical challenges persist. Thermal and power constraints remain foundational: running large models on battery-powered devices demands aggressive model compression, quantization, and dynamic voltage-frequency scaling, and sustained AI workloads can trigger thermal throttling that degrades user experience. Memory bandwidth presents a further difficulty, as on-device inference is frequently memory-bound rather than compute-bound, and the cost of high-bandwidth DRAM constrains model size in mid-tier devices. Software ecosystem fragmentation poses ongoing challenges, as AI frameworks, model formats, and NPU instruction sets vary across chip vendors, complicating developer porting and limiting the availability of compelling on-device applications. Additionally, application ecosystem maturity lags hardware capability—many advertised AI features remain narrowly scoped—and the resulting gap between marketing claims and delivered utility risks consumer disillusionment, reinforcing the importance of software differentiation among leading OEMs.

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