TLDR Tech / Practical
Inside LLM APIs: Hardware and bottlenecks explained
This technical analysis examines the hardware and performance constraints involved in running large language model APIs, using the Qwen2.5-7B-Instruct-AWQ model served by vLLM on an RTX 5080 as an example. It details the two main operational stages of LLMs—the compute-intensive prefill and the memory-bandwidth-dependent decode—and discusses how memory needs, KV caches, and scaling strategies apply from single consumer GPUs to large server clusters.
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