inference-optimization-pipeline-order

IN premise — summaries/2026/08/24/wiki-BERT_language_model-chunk-3-chunk-4.md

Created 2026-08-24T17:11:06+00:00

The LLM inference optimization pipeline follows the sequence: model compression → knowledge distillation → quantization → speculative decoding → PagedAttention → batched serving (vLLM, SGLang, TensorRT-LLM)

Summary

When you're making a large language model faster and cheaper to run, there is a specific order to apply your optimizations: shrink and simplify the model first, then reduce its numerical precision, then add fast draft-and-verify decoding, and only at the end wire it into a serving framework that batches requests together. This matters because each step builds on the one before it, so skipping ahead or reordering the pipeline wastes effort and can make later stages impossible or less effective.