What are the top edge AI chips of 2025?

Edge AI refers to deploying artificial intelligence (AI) algorithms and models directly on local devices like sensors, smartphones, or Internet-of-Things (IoT) devices. This approach includes edge computing, which processes data closer to where it’s generated rather than sending it to a centralized Cloud for analysis.

According to a report by Gartner, by the end of 2025, approximately 55% of all data analysis in deep neural networks will occur on edge devices. As a result, edge AI chips are expected to capture a significant share of the AI chip market. These chips will be applied in self-driving cars, smart homes, wearable devices, smart city infrastructure, and industrial automation.

Edge AI chips offer several advantages. They reduce latency, enabling faster response times through local processing. The need to transmit sensitive information to the Cloud is minimized by processing data locally, enhancing user privacy.

These devices can also operate with limited or no internet connectivity. Since minimal data is sent to the Cloud, bandwidth is conserved, and network congestion is reduced. Local AI data processing is also more energy-efficient than continuous data transmission to and from the Cloud.

Edge AI chips differ significantly from other AI accelerators. Their design emphasizes power efficiency, low latency, compact size, enhanced security, offline functionality, and affordability. These chips are designed for applications like IoT, wearables, smart home devices, robotics, drones, industrial automation, and autonomous vehicles.

By contrast, AI accelerators are built to handle high throughput, scalability, and network connectivity, focusing on data centers, high-performance computing (HPC) systems, cloud platforms, and deep learning. Edge AI chips function as specialized “mini-brains” for devices, enabling localized intelligent decision-making, while AI accelerators are built for processing vast amounts of data at centralized locations.

Edge AI chips face stricter power constraints than mobile AI chips, requiring greater energy efficiency and advanced power-saving techniques. Edge AI applications often have more demanding real-time processing requirements, with a focus on lower latency and faster speeds.

Unlike mobile devices, edge devices typically operate in environments with limited or intermittent connectivity, requiring chips that function effectively without consistent network access. Edge AI chips are also designed to manage sensitive data, incorporating specialized hardware and software security features to protect user information.

Additionally, they need to adapt to a broader range of tasks and environments than mobile AI chips. In this article, we’ll explore the top-edge AI chips shaping the landscape in early 2025.

The top edge AI chips

In 2025, the edge AI chip market remains dynamic and competitive, with players such as Nvidia, Qualcomm, MediaTek, and Ambarella dominating the space. Startups like Mythic and Halio are also introducing innovative edge AI solutions, challenging established companies with fresh approaches and disruptive technologies.

Nvidia’s Jetson platform is expected to launch a new version this year, featuring enhanced AI capabilities and improved performance. Currently, Nvidia’s flagship chip in the edge AI segment is the Jetson Orin. It’s known for its high performance and versatility across applications such as robotics, drones, smart cities, and industrial automation.

The Jetson Orin delivers up to 275 TOPS (Tera Operations per Second) of AI performance, enabling complex tasks like real-time object detection, 3D perception, and natural language understanding. Built on Nvidia’s Ampere GPU architecture, it features 2048 CUDA cores and 64 Tensor Cores to accelerate AI computations. The chip supports many high-speed I/O options, including PCIe Gen4, Gigabit Ethernet, and multiple MIPI CSI-2 interfaces, making it adaptable to various applications.

With power configurations ranging from 15 to 50 W, the chip is suitable for diverse power budgets and operational needs. Nvidia’s optimized software stack, including TensorRT and CUDA, further enhances its performance and efficiency.

The NVIDIA Jetson AGX Orin Developer Kit provides a comprehensive development environment for the Jetson Orin, which can also be purchased as a stand-alone module. This developer kit offers a functional platform for experimentation and application development.

Qualcomm offers diverse processors with integrated AI capabilities, including the Snapdragon, QCS, and RB5 series. The Snapdragon lineup is Qualcomm’s flagship offering, encompassing the 8, 7, and 6 series processors.

Here’s a breakdown:

The Snapdragon 8 series (e.g., Snapdragon 8 Gen 2, Snapdragon 8+ Gen 1, and Snapdragon 8 Gen 1) features a Hexagon DSP AI engine capable of handling tasks such as computer vision, natural language processing (NLP), and machine learning.
The Snapdragon 7 series (e.g., Snapdragon 7+ Gen 2, Snapdragon 7 Gen 2, and Snapdragon 778G+) balances performance and power efficiency, making it ideal for edge AI applications.
The Snapdragon 6 series (e.g., Snapdragon 6 Gen 1 and Snapdragon 695 5G) offers cost-effective solutions for budget-friendly edge devices.

The QCS series includes system-on-chips (SoCs) designed explicitly for edge AI applications, such as the QCS6490, QCS407, and QCS404.

The QCS6490 is built for high-performance edge AI tasks.
The QCS407 provides a mid-range option with balanced performance and power efficiency.
The QCS404 serves as an affordable, entry-level solution for basic edge AI applications.

These SoCs benefit from Qualcomm’s broader Snapdragon ecosystem, which includes software development tools, SDKs, and compatible hardware components.

MediaTek offers a range of processors with integrated AI capabilities designed for various edge AI applications. These include the Dimensity, Helio, and Genio series. The Dimensity series processors, such as the Dimensity 9000+, Dimensity 9200, and Dimensity 1080, are high-end chipsets primarily designed for premium smartphones.

Equipped with dedicated AI Processing Units, they handle tasks like real-time object detection, facial recognition, image enhancements, real-time translation, auto-suggestions, and voice recognition. Beyond smartphones, the Dimensity series is also used in wearables, IoT devices, and AR/VR applications.

The Helio series processors, including the Helio G99, Helio G88, and Helio G70, are optimized for mid-range and entry-level smartphones and other mobile devices. This series emphasizes gaming performance while excelling in AI-related tasks such as game AI, computer vision, and gaming optimization. The Helio series is typically used in wearables, smartphones, and gaming devices.

The Genio series processors, such as the Genio 700, Genio 500, and Genio 350, are tailored for IoT and edge computing applications. The Genio 700 is recommended for more demanding edge AI tasks that require higher performance and advanced AI capabilities. At the same time, the Genio 500 is ideal for cost-sensitive applications or those with limited budgets.

Ambarella supplies chips for computer vision, artificial intelligence, and low-power, high-definition video and image processing. Its key products come from the CVflow family, which includes the CV2 and CV5 series.

CVflow is the foundational architecture powering Ambarella’s AI chips for computer vision and AI applications. These processors feature high-performance image processing and hardware acceleration for neural networks (NNs) while maintaining low power consumption. They’re commonly used in security cameras, robotics, and advanced driver-assistance systems (ADAS). Typical applications include video analytics, facial recognition, object detection in security cameras, lane keeping and pedestrian detection in ADAS, navigation, object recognition, and obstacle avoidance in robotics.

The CV2 series — including CV2, CV22, and CV25 processors — is tailored for automotive applications, such as ADAS and autonomous driving. These chips support high-resolution video processing, streaming from multiple cameras, and AI-specific hardware. The CV5 series, featuring CV5 and CV52 processors, focuses on high-resolution video processing and advanced computer vision, offering support for 8K video and high-performance image analytics.

The CV75S is an SoC for AI-powered cameras and other edge devices. Built on the CVflow 3.0 AI Engine, it can run complex AI models such as vision language models (VLMs) and Vision Transformer Networks. It supports 8MP60 image processing and includes features such as HDR, de-warping, electronic image stabilization (EIS), and low-light imaging.

Mythic focuses on analog computing for AI, with its flagship product, the M1076 Analog Matrix Processor (AMP). The core technology behind Mythic’s edge AI chips is Analog Compute-in-Memory, which stores and processes neural network weights directly within the analog memory. This eliminates the need for constant data transfer between memory and processing units, significantly reducing power consumption and improving AI inference performance.

The chip is designed for compact and embedded systems, making it ideal for applications in computer vision, smart home devices, industrial IoT, and robotics.

The M2000 series, Mythic’s next-generation platform, is currently in development. It is expected to deliver increased processing power and throughput, support more complex AI models, and offer improved integration with existing systems.

Halio focuses on edge AI processors, with its flagship product being the Hailo-8 AI Accelerator. The chip provides up to 26 Tera-Operations per Second (TOPS) of AI processing power and features a small form factor, enabling seamless integration into various edge devices. Its unique dataflow architecture ensures high performance at low power consumption.

The Hailo-8 is well-suited for applications such as ADAS, in-car infotainment, industrial IoT, computer vision, and robotics.

Applications of edge AI chips

Edge AI chips deploy AI algorithms and models directly on local devices like sensors, smartphones, or IoT systems. They’ve evolved to meet specific requirements in various use cases, particularly for applications needing real-time processing. These chips enable immediate analysis of data generated by the device itself.

For example, in autonomous vehicles, sensor data must be analyzed in real-time for tasks like object detection, lane tracking, and collision avoidance. Similarly, industrial automation relies on real-time data processing to monitor machinery health, detect defects, and optimize production processes. In smart home devices, edge AI chips process voice commands, control appliances, and monitor for security threats locally.

Applications such as augmented reality (AR), virtual reality (VR), mixed reality (MR), and robotics require edge AI chips to minimize latency. Latency, typically caused by transmitting data to and from the cloud, is reduced through local processing. For instance, AR/VR applications must deliver immersive experiences with minimal delays, while robots must quickly and effectively respond to their surroundings.

Edge AI chips are especially valuable when privacy and security are primary concerns. Wearable medical devices, for example, analyze data to detect health issues without transmitting sensitive information. Financial transactions also benefit from fraud detection performed locally, ensuring user data is protected.

Additionally, edge AI chips are effective when network connectivity is limited or unavailable. They enhance reliability in monitoring environmental conditions, infrastructure, or industrial equipment in remote locations. Those devices equipped with AI chips can provide critical information and services during disaster response when network infrastructure is disrupted.

Conclusion

Demand for edge AI chips continues to grow due to their wide range of applications. While advancements in 5G and other connectivity technologies have accelerated the adoption of edge AI, increasing use cases in automotive, healthcare, and industrial automation are driving market growth.

Startups are also actively developing innovative edge AI chips tailored to specific verticals, while established players like Nvidia, Qualcomm, Ambarella, and MediaTek are refining their already dominant products.

The rising emphasis on data privacy and security further highlights the need for on-device AI processing across various applications, positioning edge AI chips as essential components in the evolving technological landscape.