This article discusses the challenges in LLM inference, scaling up LLMs with model parallelization, optimizing the attention mechanism, model optimization techniques, and model serving techniques. It covers topics like batching, key-value caching, memory requirements, attention optimization, and efficient KV cache management. It also mentions techniques like quantization, sparsity, and distillation for model optimization, and in-flight batching and speculative inference for model serving.
Table of contents
Understanding LLM inferenceScaling up LLMs with model parallelizationOptimizing the attention mechanismEfficient management of KV cache with pagingModel optimization techniquesModel serving techniquesConclusionSort: