Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and…
Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and nodes to scale to more users while reducing latency. Distributed inference frameworks use techniques such as disaggregated serving, KV cache loading, and wide expert parallelism. In disaggregated serving environments…
