A small language model is a language model with relatively few parameters, built to run cheaply and quickly on a single server, device or private environment.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
Edge AI is running machine learning models directly on devices or local hardware instead of a cloud server. Phones, cameras, vehicles and factory gateways are typical hosts.
Fine-tuning is further training of an existing model on a smaller, task-specific dataset so it adopts a particular style, format or skill. It changes how a model behaves without the cost of training a new one from scratch.
Inference is running a trained model to produce an output, as opposed to training it. In production this is where most of the cost, latency and scaling work of an AI feature sits.
Model quantization is reducing the numeric precision of a model's weights, for example from 16-bit to 8-bit or 4-bit values. The model then needs less memory and runs faster.
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