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On September 4, 2026, the article published by Towards Data Science stated that disaggregation of large models is not a simple technical operation; it is a complex engineering task involving thousands of GPUs. The report emphasized that although disaggregation aims to improve training efficiency and flexibility, it faces significant challenges in computing power scheduling, communication overhead, and system consistency during implementation, which become core bottlenecks in the current deployment of large-scale AI infrastructure.
This strategy yields benefits when the three conditions for separate pre-filling and decoding are met; below this threshold, block-based pre-filling is the default correct choice. The article “Disaggregation Is a Thousand-GPU Problem” was first published in Towards Data Science.