Optimizing Inference in Language Models

This chapter explores various strategies for inference optimization in large language models, emphasizing the tradeoffs between latency, throughput, and cost. Techniques such as quantization and speculative decoding are discussed to enhance performance without sacrificing quality. The importance of appropriate GPU selection and infrastructure for effective deployment is also highlighted, alongside practical insights for improving overall efficiency.