In this article
Inference is the production phase where models process real requests. Key metrics are latency (time to first token, total generation time), throughput (requests/second), and cost ($/1M tokens). Optimisation techniques include batching, KV-cache, speculative decoding, quantisation, and model parallelism. Inference costs typically dwarf training costs in production.
What it means in practice
Inference is not just vocabulary; it is a design handle. Use it as a reference point when comparing architecture choices, debugging implementation trade-offs, or explaining system behaviour to another engineer. It helps teams choose the right model/runtime balance across quality, speed, memory, governance, and cost.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Benchmarks are only a starting point. Validate with your prompts, data, latency budget, concurrency pattern, and safety requirements.
Related context
Useful neighbouring concepts: Model Serving, Quantisation, Latency.