How to choose LLM inference optimization techniques using a decision tree
Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach
This guide presents a six-step decision tree for selecting LLM inference optimization techniques, helping engineers pinpoint bottlenecks based on hardware, model size, and traffic patterns. It provides an in-depth comparison of the assumptions and trade-offs behind model compression (quantization and distillation), prefix caching, continuous batching, PagedAttention, speculative decoding, and prefill-decode disaggregation, along with troubleshooting steps for common failure modes such as zero cache hit rates and unexpected speculative decoding slowdowns.
By organizing core bottlenecks—including GPU memory, prompt structure, and compute contention—into a clear six-step decision tree, the article provides an effective framework for evaluation and troubleshooting in production LLM deployments.
Source:Hacker News · AI · machinelearningmastery.com