Running Large Language Models in Production

This post details best practices for deploying large language models (LLMs) in production, focusing on practical considerations for running these models efficiently. It covers aspects such as hardware selection (e.g., GPU types, memory), software stack optimization (e.g., inference servers, libraries), and inference techniques (e.g., quantization, batching, speculative decoding) to maximize throughput and minimize latency. The goal is to provide engineers with actionable guidance for operationalizing LLMs in real-world applications.