Speaker
Description
This presentation reviews the main algorithmic and systems-level ideas for making large-scale generative AI more efficient across the full model lifecycle: pre-training, fine-tuning, and inference. It introduces the computational challenges created by scaling laws, model size, memory footprint, and long-context generation, then surveys model-centric approaches such as quantization, pruning, low-rank approximation, knowledge distillation, mixed-precision training, sparse initialization, and parameter-efficient fine-tuning methods including adapters, prompt tuning, and LoRA-style techniques. The presentation also covers inference-time acceleration, including speculative decoding, KV-cache optimization, efficient attention variants, mixture-of-experts architectures, and long-context methods. The talk is intended for audiences interested in deploying, adapting, or studying large-scale ML models under realistic compute and cost constraints.
