Running AI where the data is created
Most AI systems work by sending data to a remote server for processing and waiting for the result. A camera detects an object, sends the image to a cloud server, the server runs a model, and the result comes back. This round trip typically takes 50 to 200 milliseconds, and it requires a reliable internet connection throughout.
Edge AI eliminates this round trip by running the AI model directly on the device where the data is collected: the camera itself, an industrial robot, a vehicle, a medical scanner, or a smartphone. This reduces response time to under 10 milliseconds and allows the device to operate without any internet connection. It also means the raw data never leaves the device, which is important for privacy and for industries where data sovereignty regulations prohibit sending information to external servers.
Vector search on edge devices
Many edge AI applications need to compare new observations against a database of known patterns: is this camera image similar to a known defect? Is this sensor reading outside the normal range? Is this person in the authorized access list?
Running a vector database on edge hardware used to be impractical because edge devices have limited memory (often 512 MB to 4 GB) and modest processors. Compression techniques like scalar quantization (which reduces memory by 4x) and product quantization (which reduces it further) have changed this. A collection of one million vectors that would require several gigabytes in full precision can fit in under 500 MB after compression, making on-device similarity search viable on commonly available edge hardware.
Real-world edge AI applications
Self-driving vehicles need to recognize objects, pedestrians, and lane markings in real time. Even a 100-millisecond delay from a cloud round trip is too dangerous: the car moves several meters in that time. Autonomous vehicles must process everything locally.
Smart factory cameras inspect products for defects at high speed on the production line, and manufacturers cannot send confidential production imagery to an external cloud. Medical devices used in remote clinics or ambulances need diagnostic AI even when no internet connection is available. Defense and intelligence systems often operate in environments with no connectivity at all. For all of these use cases, edge AI is not a preference but a necessity.