AI Inference
The process of running a trained AI model on input data to produce an output, distinguished from training (which produces the model) and fine-tuning (which adapts it).
How it works
Inference is what production AI actually does at runtime. Every prompt sent to a language model, every embedding computed, every classification produced, is an inference call. Inference cost and latency dominate the economics of production AI: a system handling 100,000 inferences per day at 0.5 seconds each is a different operational beast from one handling 10 inferences per day at 30 seconds each. Optimisation levers include model quantisation (running at 4-bit or 8-bit precision rather than 16-bit), batching, key-value caching, and choice of inference framework (vLLM, TensorRT-LLM, llama.cpp, others). The figure that matters to a business is not the cost of a single inference call but the ratio between that cost and the cost of the human task it replaces: an inference that costs a fraction of a penny can stand in for minutes of a senior professional priced at six figures a year. For on-premise UK deployments, inference engineering is where Ayoob AI's GPU compute infrastructure expertise and patent portfolio matter most, because moving inference on-device removes both the per-call API cost and the data-residency exposure of sending regulated data to a third party.
Related terms
Model Serving
The infrastructure layer that exposes a trained AI model as a network endpoint, handling request batching, load balancing, autoscaling, observability, and version management.
Large Language Model (LLM)
A neural network trained on large text corpora to predict the next token given context, used for text generation, summarisation, classification, and reasoning tasks across enterprise software.
Fine-Tuning
The process of further training a pre-trained language model on domain-specific data to adapt its behaviour, terminology, or output format to a particular use case or organisation.
Private AI
AI deployed on infrastructure the client controls (on-premise, in the client's cloud tenancy, or air-gapped), with no third-party LLM provider in the data path and no inference-time data export.
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