What is an embedded AI model? Redefining the future of on-device intelligence

It might seem as though artificial intelligence (AI) is everywhere, but it isn’t—at least not yet. At the moment, AI lives mostly in remote data centers, in places like Ashburn, Virginia; San Antonio, Texas; and Phoenix, Arizona.

AI feels like it’s everywhere, partly because the Internet of Things (IoT) has blurred the distinction between smart devices and connected devices. Most AI models are not embedded, meaning that the models are not deployed directly on a device that performs processing locally. Rather, the vast majority of AI inferencing—the process of applying a trained machine learning model to new data to generate predictions in real time—actually occurs in massive data centers with immense compute resources. Our smart devices aren’t really that smart.

This shouldn’t come as a surprise because most encounters with AI happen on the web. AI models analyze browsing, listening, and viewing habits to generate personalized content recommendations in real time. Internet search engines, which have remained relatively unchanged for years, are suddenly being supplemented (or entirely supplanted) by large language models (LLMs) that can summarize information, translate languages, and dynamically calibrate responses based on user inputs.

All of that is happening in the cloud, and it’s limiting the potential for AI to generate meaningful insights into complex systems all around us: cities, healthcare, factories, transportation, and more. We must push intelligence to the edge to facilitate AI’s latent potential. 

With the edge unlocked, AI models can perform a vast array of tasks across a widely distributed set of fields—and not just on the web. In automobiles, advanced driver assistance systems can use computer vision models to process real-time data provided by onboard cameras and sensors to detect and alert drivers about potential collisions, lane departures, and sudden changes in traffic flow. In factories, machine learning models can ingest vast quantities of sensor data—on temperature, humidity, pressure, vibration frequencies, and acoustic signatures—to prompt proactive maintenance, reduce manufacturing downtime, and protect worker safety. And AI models can be trained to detect irregular heart rhythms, blood glucose levels, skin temperatures, and respiration rates in patients wearing any number of smart devices: rings, pendants, watches, posture correctors, canes, and more—a new and emergent category of small, embedded AI devices. 

Recent breakthroughs in artificial intelligence and data science have already launched a sea change in the world of computing, and as industries begin to grasp the enormous range of possibilities before them—and use the edge to scale up those applications—what comes next won’t simply be a question of what’s faster or smarter. We’ll be experiencing a complete reimagination of computing itself.

Cloud limitations

Despite this surge in AI adoption and a proliferation of embedded devices, reliance on cloud computing restricts the capacity for using AI to solve complex problems. The cloud isn’t always a feasible choice, even at a basic level. In settings where safety and security are important, AI models must perform quickly and accurately without an internet connection. In healthcare applications, protecting patient privacy can make uploading data to a remote server undesirable. Often, sufficient network bandwidth simply isn’t available. As a result, there’s a growing need for real-time, localized decision-making, and embedded AI models are the key to unlocking the full potential of AI.

But we shouldn’t stop there. We can free up network bandwidth and compute resources in the cloud by deploying embedded AI models and moving AI inferencing to the edge. Embedded computing has transformed the digital landscape. Billions of devices generate vast amounts of data, creating opportunities for unprecedented awareness about the world we live in. The next step requires analyzing that data at the edge before sending it to the cloud for even more robust learning opportunities. 

Information must be transformed into knowledge to fully leverage the possibilities of this data-centric age. Because machine learning (ML) models are designed to identify patterns and relationships, generalize from examples, and improve performance in response to real-world data, they excel at converting raw data into higher-level knowledge. Creating a network of AI models along a continuum of devices—embedded on personal devices, fog computing nodes, local servers, and more robust compute resources in the cloud—will allow us to build what has been described as a planetary “sensory cortex.” 

Intelligence at the edge

Embedded AI models stand apart from their cloud-based counterparts because they can operate efficiently within constrained environments. These models are designed to function with minimal power consumption, limited memory, and low computational overhead, making them well-suited for devices with scarce processing resources. Embedded AI models prioritize real-time, on-device processing, allowing them to deliver instant insights without network delays.

By moving processing closer to the edge, we can increase AI’s efficiency and develop embedded intelligence about large-scale systems and environments. Next-generation AI systems will allow devices to synthesize data rather than just collect and transmit it. This transition will unlock new possibilities for automation, efficiency, and human-machine collaboration​.

The key is leveraging cloud computing’s considerable resources by maximizing efficiency at the edge. Let’s take, for example, a healthcare setting. As we suggested earlier, privacy concerns may make it impossible to send information from wearable devices—heart rate variability, ECG, physical activity levels—to the cloud for data processing. By activating the edge with embedded AI models, however, we can process data locally, right on the device.

Edge devices can use embedded AI models to generate aggregate features, anomaly scores, and local model updates, all of which can be sent to the cloud for further processing. Then, those locally embedded AI models can be updated based on patterns collected across entire populations, without raw user data ever leaving the edge device. What’s more, AI models in the cloud can be used to detect emergent patterns we might otherwise miss. In this way, we can use combinations of AI models to understand the broad set of problems Jane Jacobs described as problems of organized complexity: “multiple systems that are independent but interrelated into an organic whole.”

This is why the adoption of embedded AI models is crucial. Lightweight and highly efficient, embedded AI models are specifically designed for deployment on resource-constrained edge devices like microcontrollers, sensors, and other embedded systems. These models bring the power of artificial intelligence directly to the source of the data, enabling real-time processing and intelligent decision-making without the need for constant cloud connectivity.

Advantages of processing at the edge

Embedded AI models offer several advantages that make them well-suited for real-world applications across industries such as healthcare, industrial automation, and transportation. One of their most significant benefits is improved responsiveness—by processing data locally rather than sending it to the cloud, embedded AI enables real-time decision-making. This is critical for applications like autonomous vehicles, where milliseconds can mean the difference between avoiding a collision and an accident, or industrial automation, where immediate detection of equipment anomalies can prevent costly failures.

Embedded AI models also protect privacy. Because they process data directly on the device, sensitive information—such as patient health metrics from wearable monitors or video feeds from smart surveillance systems—never has to leave its source. This reduces exposure to cyber threats and ensures compliance with stringent data protection regulations.

Embedded AI models increase reliability because they eliminate dependence on constant internet connectivity. This makes them especially valuable in remote or high-security environments, such as off-grid medical devices that must function without cloud access or factory automation systems that must remain operational even during network disruptions.

Finally, embedded AI models reduce bandwidth usage. Because embedded AI performs inferencing locally, it minimizes the need for continuous data transmission, reducing network congestion and lowering cloud storage costs. Whether optimizing smart traffic management systems or enabling predictive maintenance in industrial settings, embedded AI ensures that only the most relevant insights are transmitted, improving efficiency across connected ecosystems.

The ability of embedded AI to simultaneously address latency, data privacy, and network bandwidth limitations makes it a compelling alternative to a centralized cloud-native solution. The scalability in operational cost and carbon footprint reduction make it even more attractive. 

Building and deploying embedded AI models

Developing embedded AI models presents unique challenges, requiring a careful balance between performance and efficiency. One of the primary hurdles is model optimization—AI models must be compressed to reduce their size and computational demands while maintaining accuracy. This ensures they can operate within the power and memory constraints of edge devices without compromising performance.

To achieve this, embedded AI models leverage advanced optimization techniques such as quantization, which reduces numerical precision to lower memory and processing requirements, and pruning, which eliminates unnecessary parameters while preserving accuracy. Additionally, embedded AI models benefit from specialized hardware accelerators like tensor processing units (TPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) to maximize efficiency. These optimizations collectively enable embedded AI models to perform sophisticated tasks—such as object detection in autonomous vehicles, anomaly detection in industrial automation, and continuous health monitoring in wearable devices—all while operating within the strict constraints of edge computing environments.

Another challenge is hardware compatibility. Embedded AI technologies must run efficiently on a wide range of devices, from low-power microcontrollers to specialized AI accelerators like TPUs and FPGAs. Ensuring seamless deployment across diverse hardware architectures requires tailored optimization techniques and cross-platform compatibility.

MLOps tools are emerging as essential solutions to simplify this complex development process. For teams looking to implement or create embedded AI solutions to integrate with existing or new products, the Latent AI Efficient Inference Platform (LEIP) provides optimization, deployment, and monitoring capabilities, allowing developers to fine-tune and deploy AI models efficiently. These tools help bridge the gap between AI research and real-world applications, making scaling embedded AI systems across industries easier. With LEIP, developers can rapidly prototype and deploy embedded AI at scale.

The future of embedded AI

As the IoT expands, embedded artificial intelligence will play an increasingly vital role in enabling smarter, more autonomous devices from self-optimizing industrial machinery to real-time patient monitoring in healthcare. AI at the edge will drive innovation across industries by providing faster insights with minimal energy consumption.

These innovations will benefit industries such as healthcare, automotive, and smart cities. Hospitals will deploy AI-driven diagnostics onsite, reducing the need for cloud-dependent analysis. Self-driving cars will rely on more efficient embedded models to process real-time sensor data. Urban infrastructure will leverage AI to optimize traffic patterns and energy usage, making cities more responsive and sustainable. As embedded AI technology evolves, its ability to enhance efficiency, privacy, and automation will reshape how devices interact with the world.

If you’re interested in exploring the possibilities of embedded AI applications, contact Latent AI to schedule a consultation and take the first step towards unlocking real-time insights at the edge. We can help you rapidly scale up your embedded AI deployment.