How Distributed Inference Scales LLMs Across Multiple GPUs

Published 13 October 2026

Distributed inference addresses model memory, growing KV caches, and high request volumes by spreading LLM workloads across GPUs. Data, pipeline, tensor, and expert parallelism, along with separate prefill and decode pools, can be combined to optimize production serving.