What is edge AI? When the cloud isn’t close enough

What is edge AI?

Edge AI is a form of artificial intelligence that in part runs on local hardware rather than in a central data center or on cloud servers. It’s part of the broader paradigm of edge computing, in which devices at the network edge — handheld devices, IoT sensors, industrial machinery, and more — process information for local use instead of forwarding it on to other nodes on the network.

Like all types of edge computing, edge AI is meant to speed up computing from the perspective of users at the edge — reducing latency and cutting down on network bandwidth use. Because AI processing happens locally, local users see results more quickly. That isn’t just a convenience: Some types of applications, like autonomous vehicle operation, simply aren’t possible if every decision requires communication to take a round trip from the edge to the cloud and back. Also, in applications that deal with sensitive data, the transmission of that data over the internet to third-parties must be kept to a minimum.

“Edge AI is crucial for integrating generative AI into human environments,” says Dani Cherkassky, CEO of Kardome, a company that makes voice AI technology for voice-enabled devices or user interfaces. “Establishing an architecture in which audio data streams 24/7 from every device to a cloud gen AI service is impractical, mainly due to significant costs, privacy concerns, and other significant challenges. Conversely, placing the entire AI agent on edge devices is computationally unfeasible.” Edge AI seeks to work in both environments to make such a service practical and feasible.

How edge AI works

There are two main processes involved in contemporary AI: training and inference. The distinction is key to understanding how edge AI works. Training, as its name implies, is the process of teaching an AI model about the world (or at least about the part of the world you expect it to reason about). Once you’ve trained that model, you can send it input and it uses what it’s learned during training to respond with output; the process by which it generates that response is called inference.

Training involves feeding a model huge datasets and teaching it how different bits of information within those datasets relate to one another. This is a computationally intensive process that requires specialized high-end processors that use a lot of energy. Individual instances of inference, by contrast, require much less computational power.

That distinction is what makes edge AI possible. In edge AI systems, the model is initially trained in a data center or in the cloud. It’s then copied to an edge device, where it performs inference. For instance, a self-driving car’s model might’ve been trained to distinguish stop signs from yield signs in the manufacturer’s data center, but the split-second decision to read a sign as one rather than the other happens in the car’s onboard computer.

Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device.

What technologies make edge AI possible?

The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality.

Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs.

Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations.

Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management capabilities to support AI workloads.

Local data processing and privacy-sensitized architecture. Edge AI leverages local data collection and inference so that sensitive data doesn’t always travel to the cloud. That capability has been boosted by advances in hardware (secure enclaves, trusted execution environments) and software (privacy-preserving ML, on-device inference libraries), making it possible to run models offline. This makes edge deployment viable in regulated industries and low-connectivity environments.

What are examples of edge AI applications?
Edge AI isn’t a single technology so much as an architectural shift — pushing intelligence closer to where data is created. That shift has moved from experiment to deployment across industries that depend on real-time insight, autonomy, and local decision-making. From industrial robots to consumer devices, these examples show where edge AI is already at work.

• Industrial: Manufacturers use edge AI for predictive maintenance and quality control, spotting faults before they cause downtime. Siemens, for instance, applies edge-based machine learning in its MindSphere platform to analyze sensor data from factory equipment and detect anomalies in real time.
• Consumer: Voice assistants and AR/VR devices now process data locally to improve responsiveness and privacy. Apple’s on-device Siri processing and Google’s Tensor-powered Pixel phones both run inference on the handset rather than sending every command to the cloud.
• Healthcare: GE Healthcare’s Edison platform supports deployment of AI across imaging and diagnostics workflows.
• Transportation: Tesla now directs its onboard AI inference systems and custom chips to run autonomous driving tasks on-vehicle in real time.
• Retail: In 2024 Aldi launched a checkout-free shopping experience called “ALDIgo,” using computer vision cameras and on-device AI to track items and let customers leave without visiting a traditional checkout.

What are the advantages and disadvantages of edge AI?

Our discussion so far has laid out many of the benefits of edge AI systems:

• Proximity-based inference helps deliver faster outcomes because analytics data and other AI content is generated locally, rather than everything being routed through a distant cloud.
• Local processing diminishes bandwidth burden and data-transmission costs. The amount of data sent to a central system is, depending on the circumstances, limited or nonexistent.
• By data and analytics closer to the source, you can mitigate privacy and regulatory exposure by reducing reliance on external networks and third-party processing.
• Edge devices offer resilience: they can continue operating when connectivity to central systems falters, maintaining service continuity in remote or unreliable network environments.

That said, Edge AI is of particular value in specific types of environments where needs for low latency and enhanced privacy are paramount. But such environments come with challenges:

• Constrained local resources force trade-offs. Smaller models running on constrained hardware may be less agile and may produce less accurate results compared with alternatives running in the cloud or on more powerful processors.
• Because edge AI systems run inference locally, each device may interact with slightly different data and conditions, leading to model drift and fragmented intelligence. Without frequent synchronization or retraining, those local models can diverge from the global version, producing inconsistent predictions or degraded accuracy over time. Maintaining alignment across multiple decentralized models is a challenge unique to AI workloads at the edge.
• Remote and distributed edge environments generally lead to complex operations overall. Edge AI developers will learn what edge computing vets already know, which is that a highly distributed IT ecosystem is a tricky place to build, deploy, and maintain advanced infrastructure.

Edge AI isn’t a replacement for cloud or centralized AI applications, but is an increasingly important part of the AI ecosystem. By distributing intelligence to the point of action, it makes real-time systems — from cars to cameras to factory lines — more responsive and adaptive. Building those systems also exposes a new frontier of engineering and operational complexity. As edge AI matures, its success will depend on how well organizations manage that complexity across thousands of distributed devices.