A Practical Deep Dive on LLM Inference and Optimization!

Part 13 of an LLMOps crash course focused on LLM inference and optimization. Covers the prefill and decode phases, KV caching and its optimizations (PagedAttention, prefix caching), attention-level techniques (FlashAttention, GQA), speculative decoding, model parallelism strategies, and hands-on comparisons between vLLM and standard inference. Emphasizes that inference optimization is critical for production deployments where costs, latency, and memory constraints determine whether a model is actually usable at scale.