The engineering questions you get asked, and the bugs you actually hit. Drawn, one mechanism at a time, in the order the videos ship.
How 4-bit quantization (AWQ, GGUF) and Low-Rank Adaptation (LoRA/QLoRA) slash AI memory requirements by 80% without degrading model intelligence.
A deep dive into vector embeddings, high-dimensional cosine distance, and how Hierarchical Navigable Small World (HNSW) graph indexes achieve sub-10ms semantic search across millions of vectors.
A comprehensive deep dive into Key-Value (KV) caching, PagedAttention memory management, and cross-request prompt prefix caching for LLM production serving.
How knowledge distillation transfers dark knowledge, logits distributions, and reasoning capability from massive teacher models into compact, edge-ready student models.
