How LLM Inference Works

When you enter a prompt into an LLM, the model converts your text into numbers, processes them, and returns a response one token at a time. In this article, we go through the journey of LLM inference and see how it works. LLMs are just neural networks built on the transformer architecture. Unlike earlier architectures that processed text sequentially, transformers can analyze entire sequences in parallel, making them more efficient to train and deploy. The fundamental building block of these models is the transformer layer, which consists of two primary components: LLMs stack dozens of these layers, creating deep networks capable of capturing complex patterns in language. Transformers rely on self-attention and it evaluates how each word relates to the rest of the sequence, not just its neighbouring words. Model size = the number of parameters in the network. A 7-billion parameter model has 7 billion floating-point numbers that store the learned knowledge from training. These parameter

How LLM Inference Works How LLM Inference Works Arpit Bhayani engineering, databases, and systems. always building. When you enter a prompt into an LLM, the model converts your text into numbers, processes them, and returns a response one token at a time. In this article, we go through the journey of LLM inference and see how it works. What are Large Language Models? LLMs are just neural networks built on the transformer architecture . Unlike earlier architectures that processed text sequentially, transformers can analyze entire sequences in parallel, making them more efficient to train and de

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