Disaggregated LLM Inference

An LLM inference architecture paradigm that deploys the compute-intensive prefill phase and the memory-bandwidth-intensive decode phase on separate GPU resource pools, enabling independent optimization of hardware configuration and batching strategies for each phase to improve overall inference efficiency and service quality.