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Life of an inference request (vLLM V1): How LLMs are served efficiently at scale
vLLM is an open-source inference engine that serves large language models. We deploy vLLM across GPUs and load open weight models like Llama 4 into it. vLLM sits at the intersection of AI and systems programming, so we thought that diving into its details might interest our readers.
In this blog post, we describe how an inference request travels through vLLM’s OpenAI-compatible API server and core engine. KV Cache: The GPU memory region used to store the transformer attention keys and values for each token in a request. For requests in the decoding phase, the output tensor from the final transformer layer produces the logits, which represent the predicted probabilities for the next token.
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