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.

In agentic AI, log every step. Every LLM call, tool call, trace, span, decision, input, and output. Log the reasoning signals wherever possible. If you can’t observe what your agent is doing, you can’t reliably debug it.