Inference Framework

Inference frameworks encompass methods for efficiently extracting information and making predictions from complex models, primarily focusing on optimizing computational resources and improving accuracy. Current research emphasizes scaling inference compute through techniques like repeated sampling, sparse attention mechanisms, and efficient model architectures such as Mixture-of-Experts (MoE), aiming to balance speed and accuracy across diverse applications. These advancements are crucial for deploying large language models and other computationally intensive AI systems in resource-constrained environments and for improving the efficiency and reliability of AI-driven decision-making.