Designing distributed AI inference: Core concepts and scaling dimensions

Examines how to design distributed AI inference systems by establishing core vocabulary, analyzing prefill versus decode workload phases, and mapping five dimensions of parallelism: tensor, pipeline, expert, data, and context. Introduces a decision framework connecting business requirements to infrastructure choices and covers ecosystem developments including CNCF acceptance of llm-d, KV cache management, and speculative decoding, using Alibaba’s Qwen model families as examples.