llm-inference-optimization-techniques

Summary

There is a practical toolkit of methods for making large language models run faster and on less hardware, including shrinking models, transferring their knowledge into smaller versions, and scheduling memory and compute more cleverly during text generation. This matters because it defines the concrete levers a system can pull to cut inference cost and latency instead of accepting that bigger models always demand proportionally more resources.