Inference Speedup

Inference speedup in large language models (LLMs) and other deep learning architectures is a crucial research area aiming to reduce computational costs and latency without sacrificing accuracy. Current efforts focus on techniques like structured pruning, prompt engineering (internalizing prompts, skill-localized tuning), and novel decoding methods (early exit, speculative parallel decoding) to achieve significant speed improvements. These advancements are vital for making these powerful models more accessible and efficient for real-world applications, impacting fields ranging from natural language processing and computer vision to speech recognition and causal inference.