WHY IS EDGE AI GROWING SO FAST?
AI technology is driving a new golden age of artificial intelligence. By bringing AI processing directly to everyday devices such as smartphones, PCs, autonomous vehicles and robots, Edge AI solutions dramatically reduce energy consumption, enhance data privacy and minimise latency compared to traditional cloud AI methods.
For AI to thrive at scale, it needs to run on everyday devices (edge devices) like PCs, smartphones, cars and robots, not just on remote servers (in the cloud).
If you’re new to this technology, start by understanding what Edge AI is and why it matters for businesses today.
WHY EDGE AI IS RAPIDLY GROWING
ENERGY CONSUMPTION
Cloud AI consumes significantly more energy compared to Edge AI technology due to larger, more complex models running on less optimised processors. Edge AI processors, specifically designed with battery efficiency in mind, greatly reduce energy demand. As data centre energy consumption escalates, efficient alternatives like Edge AI become critical, offering substantial energy savings and scalable AI deployment.
The cumulative impact of AI on energy markets is huge. The Department for Energy in the USA estimates that by 2030, data centres will consume up to 9% of the country’s electricity. Cloud operators are signing nuclear deals to boost the supply of energy – particularly carbon-free energy to make sure that the expansion of AI doesn’t come at the expense of our plant. However, it is unlikely that cloud AI supply will scale at the same rate as AI demand. Alternative approaches, such as Edge AI, are essential.
NETWORK CONNECTIVITY
Edge AI excels where network connectivity is limited or latency sensitive. Unlike cloud AI, which depends on robust internet connectivity to transmit data, Edge AI processes information locally. This is crucial for applications such as agricultural robotics operating in remote locations or autonomous vehicle safety systems requiring instant decisions, eliminating delays associated with data transmission.
However, Edge AI still faces specific challenges that need addressing, particularly around performance and efficiency. Our VP of Product Management, Kristof Beets, discusses these challenges in this short interview.
Other considerations from a network perspective include, firstly, not every device being able to connect to the internet continuously, such as an agricultural robot detecting plant and soil health in a remote field. Secondly, network transmissions introduce latency, which some applications, such as crash avoidance in autonomous vehicles, may not tolerate. Processing the data locally using Edge AI techniques may be preferable.
PRIVACY
Edge AI provides a clear advantage in data privacy, allowing individuals and businesses to retain control over sensitive information such as health or financial data. By eliminating the need to transmit personal data to third-party cloud services, Edge AI significantly reduces security risks, making it a preferred solution for privacy-conscious applications.
Explore our detailed comparison of Edge AI vs Cloud AI to learn why businesses are increasingly adopting Edge solutions.
EDGE AI MARKET GROWTH AND FUTURE FORECASTS
A new, detailed report released from the SHD Group in collaboration with the Edge AI and Vision Alliance provides fresh analysis on the applications, processors and ecosystem of the Edge AI market. It highlights AI as a compelling opportunity for edge SoC design and anticipates it to be a primary driving force for all SoCs in the market for an extended period, forecasting the following growth from Edge AI:
According to SHD Group’s findings, Edge AI market penetration is expected to surpass 31% by 2030, reaching nearly 9 billion shipments with a robust CAGR of 25.4% (2025-2030). Revenues from Edge AI devices are projected to exceed $100 billion by 2030, capturing 55% of the overall AI market. The computing segment alone will generate over $43 billion, while the industrial sector will lead in unit shipments.
The smart home market emerges as the fastest-growing segment, boasting a CAGR of approximately 40% in both revenue and units shipped.
Edge AI technology adoption will expand rapidly across major global markets, with the USA, China, and Asia-Pacific regions each experiencing approximately 21% CAGR in Edge AI revenues between 2024 and 2030. By 2030, China will lead in total market revenues at around $27 billion, closely followed by Asia-Pacific ($25 billion) and the USA ($24 billion), underscoring the global momentum behind Edge AI solutions.
You can request access to the full report here.
WHAT PROCESSORS ARE USING EDGE AI?
Edge AI processors (SoCs) are increasingly complex, integrating billions of gates, numerous IP blocks and multiple operating systems to handle sophisticated AI workloads efficiently. GPUs, CPUs and NPUs dominate this market, alongside FPGAs and DSPs. Imagination, as a leading provider of GPU IP for Edge AI, is positioned to capture significant market share, with the Edge GPU market projected to surpass $17 billion by 2030.
Learn more about Imagination’s Edge AI & Compute solutions.
The widespread use of semiconductor IP (SIP) in SoC design means the total worldwide AI SIP market, including GPUs, is forecast to grow at a 23.2% CAGR, reaching over $2 billion by 2030, with GPU IP capturing a 14% share of that market.
For more details about optimal hardware choices, explore our guide on which processors best for Edge AI.
THE FUTURE IS EDGE AI: INNOVATIONS IN PROCESSORS AND APPLICATIONS
The explosive growth of Edge AI is revolutionising industries worldwide, from industrial computer vision and autonomous vehicles to smart home devices and educational tools. Driven by innovation in processor IP and software, Edge AI technology empowers businesses and consumers globally, offering unmatched benefits in energy efficiency, privacy and real-time responsiveness.
Explore what we have to day further and discover more about Edge AI, explore our comprehensive range of Edge AI & Compute solutions and stay informed about the latest innovations.
