What is Inference? - Definition & Meaning

Definition

Inference is the phase in which a trained AI model generates predictions or output for new, unseen input. The model uses learned weights to map from input to output, without further training.

Technical explanation

Inference involves passing input through the network (forward pass) to produce output. For LLMs, this happens autoregressively: each generated token is added to the context for the next. Key considerations: latency (time to first token, time per token), throughput (requests per second), and cost. Optimizations include model quantization (INT8/INT4), batching, KV-cache for LLMs, and speculative decoding. Inference can run on-premise, in the cloud, or at the edge. Serverless inference scales automatically with demand.

How AVARC Solutions applies this

AVARC Solutions optimizes inference for production AI. We choose the right deployment option (cloud API, self-hosted, edge) based on latency and cost requirements, implement caching and batching where possible, and monitor performance for a consistent user experience.

Practical examples

• A chatbot performing inference on an LLM to generate responses to user questions.
• A fraud detection system running real-time inference on transactions to compute risk scores.
• A product recommendation API performing inference on an embedding model to find similar items.

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