Mastering LLM Techniques: Inference Optimization | NVIDIA Technical Blog

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• Mastering LLM Techniques: Inference Optimization | NVIDIA Technical Blog

  Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human emergent abilities on a wide range of language tasks. These foundation models are expensive to train, and they can be memory- and compute-intensive during inference (a recurring cost). The most popular large language models (LLMs) today can reach tens to hundreds of billions of parameters in size and, depending on the use case, may require ingesting long inputs (or contexts), which can also add expense. 

This post discusses the most pressing challenges in LLM inference, along with some practical solutions. Readers should have a basic understanding of transformer architecture and the attention mechanism in general. It is essential to have a grasp of the intricacies of LLM inference, which we will address in the next section.