LLM Inference Engineering - Problem and Solution Problem: LLMs are slow Solution: KV Cache - Avoid recomputing previous tokens. Problem: KV Cache consumes huge memory Solution: PagedAttention - Manage KV memory efficiently. Problem: GPU is underutilized Solution: Continuous Batching - Dynamically add/remove requests. Problem: Token generation is sequential Solution: Speculative Decoding - Generate and verify multiple tokens together. Problem: Attention requires large memory usage and expensive memory movement Solution: FlashAttention - Reduce memory usage and memory movement during attention. Problem: KV Cache is still large Solution: MQA / GQA - Share K/V across multiple query heads. Problem: Many optimizations need to work together Solution: vLLM - Build an efficient inference engine. Problem: Applications repeatedly process the same prefixes Solution: RadixAttention / Prefix Caching - Reuse previously computed work. Problem: Models are too large Solution: Quantization - Reduce model memory and computation. Problem: Large models are expensive Solution: Distillation / SLMs / MoE - Make inference itself cheaper. Keep Learning, Keep Sharing, and Keep Growing.