The AI hardware market in 2026 is writing a story of 'mass adoption.'

On May 11, ByteDance increased its 2026 AI capital expenditure plan from 160 billion yuan to over 200 billion yuan, a more than 25% rise, with a larger share allocated to domestic AI chips. Meanwhile, LuoTu Tech forecasts that China's consumer-grade AI hardware market (excluding phones and cars) will surpass 12.7 trillion yuan in 2026 and reach 25.6 trillion yuan by 2030.

If these figures represent the 'above-water' view of the AI hardware industry, then chips are the 'below-water' foundation supporting it all. Edge AI chips are one of the fastest-growing segments in the global semiconductor industry, with strong demand from emerging hardware categories like AI glasses, AI earbuds, and AI toys. Without technological breakthroughs and cost reductions from these chip manufacturers, the trillion-yuan narrative of AI hardware would not exist.

01

From Cloud to Edge: The Terminal Explosion

Why is AI hardware experiencing a concentrated boom in 2026? The answer lies in a keyword: the inflection point of edge computing power.

Katuzian, Executive Vice President of Qualcomm Technologies, pointed out that the bottlenecks of cloud-based AI are becoming increasingly apparent: latency, privacy issues, and lack of contextual awareness make the traditional 'request-response' model difficult to integrate into the details of daily life. 'Powerful edge AI needs to be combined with cloud AI—tasks requiring instant response, high privacy, and contextual awareness, such as wake word detection, real-time translation, and health monitoring, are completed on the device; while complex reasoning requiring massive knowledge graphs or ultra-large models is handled by the cloud,' he said.

This judgment is being fully realized at the chip level in 2026. In March, Qualcomm released the Snapdragon Wearable Platform Premium Edition—the world's first personal AI wearable platform capable of running across WearOS, Android, and Linux systems, and the first time Qualcomm has introduced the 'Premium Edition' brand to the wearable sector. The platform uses a 3nm process, features a dedicated Hexagon NPU and low-power eNPU dual-core AI acceleration architecture, supports running models with up to 2 billion parameters directly on the edge, compresses the first token generation time to 0.2 seconds, and achieves inference speeds of up to 10 tokens per second. Previously, wearable chips only had embedded NPUs capable of handling 'always-on' tasks like keyword detection and motion recognition; the Snapdragon Wearable Platform Premium Edition significantly raises the ceiling for edge AI by introducing a dedicated NPU.

Meanwhile, architectural innovations at the chip level are accelerating. Actions Technology's edge AI audio chip ATS362X adopts a CPU+DSP+NPU tri-core heterogeneous architecture, with NPU theoretical computing power reaching 132 GOPS@500MHz based on in-memory computing technology, achieving a native energy efficiency of 6.4 TOPS/W@INT8, which can be further improved to 19.2 TOPS/W@INT8 through sparse model optimization. This means high-intensity real-time computing on the edge is guarantee (ensured) while power consumption is controlled at industry-leading levels, perfectly matching the long-battery-life needs of battery-powered devices. Multiple internationally renowned brands have adopted the ATS362X series chips for their 2026 new AI speakers.

Rockchip has introduced the edge computing co-processor RK182X, featuring self-developed high neural network computing power capable of efficiently supporting the deployment of text-based LLMs and multimodal VLM models with mainstream edge parameters like 3B and 7B; the next-generation product RK1860 will significantly exceed 40 TOPS in computing power, supporting the deployment of 13B parameter-scale models; the next-generation flagship chip RK3688 is advancing front-end design. Recently, it also launched the RK3572 for the mid-range AIoT market, adopting an 8nm advanced process, integrating dual-core Cortex-A73 big cores and hexa-core Cortex-A53 little cores, with a built-in 4 TOPS NPU, delivering over 100% performance improvement and more than 50% power consumption reduction in typical scenarios compared to the previous mid-range platform.

More notably, a fundamental shift in industry trends is underway. According to IDC's latest 'Global Edge AI Chip Market Report Q1 2026,' global edge AI chip shipments increased by 78% year-on-year, but computing power growth for flagship chips slowed to 22%, while shipments of mid-to-low-end AI chips for IoT, edge terminals, and industry scenarios surged by over 110% year-on-year. East Money Securities' research report further points out: The evolution of edge models focuses on two core dimensions—multimodal zero-latency interaction and algorithmic compression—the former determines the naturalness of user experience, while the latter determines the feasibility of product power consumption and cost. Only when these two paths are realized at the chip level does the inflection point for AI hardware truly arrive.

02

Computing Power Descends: From 'Hearing' to 'Understanding'

Among all AI hardware categories, voice-centric products are quietly leading the way, driven by the core force of computing power descending from the cloud to the edge at the chip level.

According to The Business Research Company, the global AI earbuds market is expected to reach USD 7.42 billion in 2026 and USD 17.34 billion by 2030. The core growth driver is shifting from the 'built-in large model' tech label to tangible scenario value. The true catalyst for this track (sector) is OpenAI's first hardware product—the AI earbuds codenamed 'Sweetpea,' designed by Apple's former Chief Design Officer Jony Ive, with estimated of first-year shipments 40-50 million units, directly competing with Apple's AirPods series. This device uses a 2nm process smartphone-grade chip, with most AI inference completed locally without cloud dependency. This sends a clear signal: AI earbuds are becoming an independent new category of intelligent terminals.

iFLYTEK is also a standout in this track (sector)—its AI conference earbuds Pro3, powered by the viaim brain, can not only automatically generate meeting titles, key point summaries, and to-do lists but also customize personalized summaries based on industry characteristics like finance and law. In terms of noise reduction and sound pickup, the 'air conduction + bone conduction' dual sound pickup system has become the mainstream solution for professional scenarios—bone conduction sensors lock in human voices by capturing vibrations from the wearer's skull, filtering out background noise at the source, and combining pre-stored personal voiceprint features. All these real-time requirements depend on the strong computing power and low-latency architecture of edge chips.

Chip manufacturers' investments in the audio track (sector) confirm its strategic value. Bestechnic focuses on designing low-power wireless computing SoC chips, with the BES2700 positioned as an ultra-low-power high-performance smart wearable SoC, already used in projects like Xiaomi's AI glasses; the new-generation smart wearable chip BES2800 is widely used in client TWS earbuds, smartwatches, and other terminal products; the BES6000 series chips, expected to be sampled in the first half of this year, adopt a single-chip A+M core heterogeneous architecture, focusing on enhancing multimodal interaction experiences.

Now, consider recording devices. AI recording hardware, represented by Plaud, has annual revenue of USD 250 million, achieving 10x growth for two consecutive years, with global sales surpassing one million units. An AI recording pod priced at RMB 899 compresses the process of 'one hour of listening, two hours of organizing' into 'generating meeting minutes in ten minutes post-meeting.' The AI-ification of recording devices essentially relies on the descent of edge speech recognition and natural language processing capabilities. Early recorders could only do linear recording; after AI-ification, devices need to perform real-time voice activity detection, speaker separation, and key information extraction in real-time. While these tasks require less computing power than visual models, for battery-powered devices extremely sensitive to power consumption, chip energy efficiency remains the decisive constraint. This is where the market value of low-power AI audio SoCs like Actions Technology's ATS362X and Bestechnic's BES series chips lies. From a tool perspective, recording chips and AI earbud chips both belong to the ultra-low-power AI voice chip track (sector), which had a global market size of approximately USD 1.686 billion in 2025 and is expected to reach USD 4.766 billion by 2032. Domestic manufacturers like Bestechnic and Actions Technology are increasingly occupying important positions in this field.

03

AI Glasses: From 'Proof of Concept' to 'Chip-Driven'

If voice hardware is the clearest growth pole in 2026, then AI glasses best embody the proposition of 'chip-driven' innovation.

IDC predicts that China's smart glasses market shipments will reach 4.51 million units in 2026, a 78% year-on-year increase, with global shipments surpassing 23 million units. Benefiting from Meta and Chinese brands actively expanding overseas markets, global AI glasses shipments are estimated to double in 2026 compared to 2025, reaching 17 million units. Behind this growth is the leapfrog progress made by the chip supply chain over the past 18 months.

Qualcomm's Snapdragon Wearable Platform Premium Edition is one of the core driving forces. Through a three-tier task distribution model, it assigns computing tasks to the most suitable nodes: lightweight tasks like adjusting settings and quick answers are run locally on 2 billion parameter models; more complex tasks are offloaded to smartphones connected via Bluetooth or Wi-Fi, capable of running 7-10 billion parameter models; only the most difficult tasks are uploaded to the cloud. This 'cloud-edge-device' three-tier computing collaboration architecture gives AI glasses unprecedented edge AI capabilities while ensuring battery life. Qualcomm expects personal AI devices to achieve scaling (scale) by 2027-2028, potentially reaching ten billion units.

Chinese chip manufacturers are also moving swiftly in the AI glasses track (sector). Rockchip's RK3588 and RK3566/RV1106 chips, with their strong general-purpose computing power and performance, have been used as main control chips in Xiaomi's AI glasses and Xvisio Technology's AR glasses. Rockchip predicts that edge AI will achieve multi-point breakthroughs across various AIoT fields in 2026 and enter a multi-year high-growth period; the company's next-generation flagship chip plans to focus on the AI glasses market, with related technologies currently under full development. Bestechnic has invested heavily in R&D to create dedicated chips for AI smart glasses, with its self-developed 6nm SoC empowering the future of edge AI; its BES2800 chip has been adopted by multiple smart glasses projects. Allwinner Technology's V851 and other V-series chips have achieved mass deployment in AI glasses, security, and other fields. The continuous descent of edge computing power is accelerating the transition of AI glasses from 'geek toys' to 'everyday essentials.'

04

Emotional and Imperceptible: AI Toys and Smart Rings

AI toys and smart rings represent another fascinating dimension—the hardware realization of emotional intelligence and imperceptible monitoring.

According to the Guangdong Toy Association, China's AI toy market reached RMB 29 billion in 2025 and is expected to surpass RMB 100 billion by 2030, with a CAGR exceeding 20%. Since 2025, AI toy-related financing has totaled over RMB 20 billion. What truly enables AI toys to undergo a 'qualitative change' is the breakthrough in lightweight large model deployment capabilities at the chip level. Rockchip's edge computing co-processor and Allwinner's AI SoC chips are providing the computing power foundation for AI toys to evolve from 'voice toys' to 'emotional companions.' Among Allwinner's AI chip products, the A733 has achieved mass production in tablets and various industry applications, while its V-series chips are also widely used in AI toy scenarios. Chengdu Huawei's newly launched 32-bit RISC-V ultra-low-power MCU targets ultra-lightweight applications like AI toys, enabling simple local data intelligence analysis and decision-making at extremely low power consumption.

A noteworthy case is the Japanese AI pet mirumi, which became a hit at CES 2026—it has no voice interaction or visual sensors; its entire function is to create a perception of 'being accompanied.' The chip logic behind this is starkly different from traditional AI chips that pursue extreme TOPS: mirumi-type products require not higher computing power but lower power consumption, more precise sensor fusion, and longer battery life.

Smart rings represent another dimension of 'imperceptible intelligence.' The global smart ring market was approximately USD 698 million in 2025 and is expected to grow to USD 7.8 billion by 2035, with a CAGR of 25.4%. These functions rely on the development of ultra-low-power sensor fusion chips—rings are compressed to 2.5mm thickness with limited battery capacity, requiring heart rate monitoring, acceleration detection, Bluetooth communication, and basic AI inference to be completed at milliwatt-level power consumption. Qualcomm's Snapdragon Wearable Platform Premium Edition has demonstrated dedicated AI acceleration capabilities for such devices, while manufacturers like Nordic Semiconductor are integrating NPUs into ultra-low-power Bluetooth chips, enabling AI inference performance 15x higher and energy efficiency 8x better than CPU solutions for wearables.

05

Conclusion

The AI hardware boom in 2026 has shed the conceptual hype of large models from previous years and truly landed in chips, power consumption, scenarios, and everyday use by ordinary people. Whether it's AI earbuds, AI glasses, or the quietly rising smart rings and emotional AI toys, the explosion of all hardware forms essentially depends on breakthroughs in edge chip computing power and energy efficiency. The cloud handles vast knowledge bases and complex reasoning, while the edge enables real-time interaction and privacy protection—cloud-edge collaboration has become the industry norm. When AI no longer requires deliberate wake-up words or cloud dependency, seamlessly integrating into life in an imperceptible, silent, and companionable way, it signifies that this intelligent revolution has truly completed its journey from tech novelty to mass-market adoption.