Edge Computing: AI That Processes Data On-Site

Every camera, every sensor, every microphone produces data. The question is no longer where to store it. It is where to process it. For years, every piece of data went to the cloud. A distant data center analyzed it, then sent back the answer. That round trip cost time. By the early 2010s, this model showed its limits. Latency and bandwidth were becoming bottlenecks. Too much data to move. The idea of edge computing reverses the logic. We no longer move the data toward the intelligence. We bring the intelligence closer to the data. The turning point comes around 2020, with the Internet of Things. Billions of connected devices produce data continuously. The central cloud can no longer keep up. We have to decentralize. Process the data where it is born, on the device itself. But one obstacle remained: AI models were too heavy. A large model demands enormous computing power. Impossible to fit onto a small camera or a sensor. They had to be shrunk. Since 2024, two techniques have changed the game. The first is called quantization. It reduces the precision of the model's weights, without breaking its accuracy. A finely coded weight becomes coarser, but sufficient. The model then requires less memory and less computation. It becomes executable locally. The second technique reduces the size of the model. It removes the redundant parts of the network. We keep what matters, we discard the rest. The result: compact models, designed to run on the device. Microsoft releases the Phi family. Meta releases Llama, available in reduced versions. These models work without a permanent connection to the cloud. The data no longer leaves the site. And the answer arrives in a few milliseconds. 5G amplifies the movement. It offers very low latency and wide bandwidth. Exchanges become nearly instant. The hardware has followed this requirement. NVIDIA's Jetson modules carry the computing power. They run inference directly on the device. Qualcomm does the same with its Snapdragon chips. The intelligence is no longer in a distant server. It fits in the palm of your hand. On the software side, two platforms orchestrate all of this. AWS Greengrass, from Amazon, and Azure IoT Edge, from Microsoft. They deploy and manage the intelligence at the edge. The uses are concrete. In healthcare, a medical device analyzes the patient's data in real time. The intervention becomes faster and more precise. In automotive, the self-driving car decides in an instant. It processes the data from its sensors on the spot. No round trip to a distant server. In industry, smart sensors monitor the production line. They adjust the pace in real time. Costly stoppages are reduced. But this decentralization has a downside. The more the data is processed locally, the more it is exposed. Security becomes a central issue. Two other challenges remain. The devices are heterogeneous and struggle to talk to each other. And their energy consumption stays hard to control. This need to keep control of the data, we share it. Koaee designs conversational assistants for companies. The question of where processing happens arises here too. Our assistants rely on servers located in Europe. No conversation is kept. The data stays as close as possible to its use. The processing draws on European and American models. Mistral, with Voxtral, and OpenAI, with GPT. Every piece of data moves under control, as at the edge. The film gives an overview, the article details all of this. You will find it in full, in the comment of the video. The link is waiting for you there. If artificial intelligence interests you beyond this topic, subscribe to the channel. And if this breakdown was useful to you, leave a like. Bringing the intelligence closer to the data: that is the whole promise of the edge. And a little of ours too. See you very soon on koaee.ai.