Inference Optimization

Inference optimization focuses on improving the speed and efficiency of running large language models (LLMs) and other foundation models, reducing computational costs and latency without sacrificing accuracy. Current research emphasizes techniques like model compression, optimized attention mechanisms, and novel sampling strategies (e.g., speculative sampling), often implemented on specialized hardware (AI accelerators) or even CPUs for resource-constrained environments. These advancements are crucial for deploying powerful AI models in real-world applications, making them more accessible and cost-effective across diverse industries, from software development to medical imaging.