Efficient Language Model Inference

The field studies how to serve language models with less computation, memory, time, and energy while preserving useful outputs. Approaches include quantization and sparsity, faster decoding such as speculative and parallel generation, KV-cache and context compression, and smaller or specialized models routed to tasks they can handle. For reasoning models, early stopping and adaptive compute allocation aim to spend more tokens on difficult questions and fewer on easy ones. The central challenge is that savings depend on the model, workload, hardware, and serving setup, and reducing token counts or latency does not always reduce total cost or energy without harming accuracy or reliability.