Edge AI
AI/dictionary/edge-ai
Definition
Running model inference on the device the data is captured on (phone, camera, sensor) rather than sending it to a server. Models are usually quantized and under 500M parameters. Latency is 5-50ms because there is no network in the loop. Powers Face ID, on-device speech recognition, doorbell person detection.
Related terms
Posts that use this term
- What leaves your machine when you use AI
What providers actually see, log, and retain when you call an LLM API in 2026. What 'we don't train on your data' really means, free vs paid tier differences, and when local is the only safe option.
- What it takes to run a model on your machine
Why VRAM is the hard ceiling on local LLMs, what quantization actually does to a model file, and the practical hardware ladder from 8GB laptops to 192GB workstations.
- The major LLMs in 2026
A tour of the closed frontier models (Claude, GPT, Gemini) and the open weights (Llama, Qwen, DeepSeek, Mistral). What 'B' means, what each is good at, and which size to actually run.
- Where AI actually runs: cloud, local, edge
Where the model file actually sits when you use AI: a datacenter GPU (cloud), your own machine (local), or the device's silicon (edge). The trade-offs and how to pick.