Inference Optimisation

Inference optimisation refers to the collection of techniques used to improve the speed, efficiency, and cost-effectiveness of running AI models in production. While training a model is a one-time (or periodic) cost, inference — running the model to generate predictions — is an ongoing expense that scales with usage. Key optimisation techniques include quantisation (reducing numerical precision from 32-bit to 8-bit or 4-bit), pruning (removing unnecessary model parameters), distillation (training a smaller model to mimic a larger one), batching (processing multiple requests together), caching (storing and reusing common results), and hardware-specific compilation (optimising models for specific GPU or CPU architectures). For large language models, specialised techniques include KV-cache optimisation (efficiently managing the key-value cache during auto-regressive generation), continuous batching (dynamically batching requests of different lengths), speculative decoding (using a smaller model to draft tokens verified by the larger model), and prefix caching (reusing computations for shared prompt prefixes).

For any AI application serving real users, inference cost and latency are critical business metrics. A model that is too slow frustrates users and limits adoption. A model that is too expensive to run at scale undermines the business case for AI. Inference optimisation can reduce costs by 50-90% and latency by 2-10x, often with minimal impact on output quality. For a business spending significant sums on AI inference (which scales linearly with usage), even modest optimisations translate to substantial savings. The right optimisation strategy depends on the application's requirements. Real-time applications prioritise latency. High-volume applications prioritise throughput and cost. Quality-sensitive applications must carefully evaluate the impact of optimisations on output accuracy. A phased approach — starting with the simplest optimisations and measuring impact — is recommended.