Diffusion Transformers (DiTs) have shown remarkable performance in generating high-quality videos. However, the quadratic complexity of 3D full attention remains a bottleneck in scaling DiT training, especially with high-definition, lengthy videos, where it can consume up to 95% of processing time and demand specialized context parallelism.

Disaggregating the generation and training stages in RL is widely adopted to scale LLM post-training. There are two critical challenges here. First, the generation stage often becomes a bottleneck due to dynamic workload shifts and severe execution imbalances. Second, the decoupled stages result in diverse and dynamic network traffic patterns that strain the conventional static fabric.

Large language model (LLM)-based applications consist of both LLM and non-LLM components, each contributing to the end-to-end latency. Despite great efforts to optimize LLM inference, end-to-end workflow optimization has been overlooked. Existing frameworks employ coarse-grained orchestration with task modules, which confines optimizations to within each module and yields suboptimal scheduling decisions.

A prominent challenge in serving requests for NLP tasks is handling the varying length of input texts. Existing solutions, such as uniform zero-padding and compiler support, suffer from either computational inefficiency or suboptimal latency. To address these practical issues, we propose an approach called polymorphing. Polymorphing involves creating and utilizing multiple runtimes of the model, each statically compiled with a different input length, to serve requests accordingly. This fine-grained use of statically-compiled runtimes reduces the overheads of zero-padding while improving latency performance compared to dynamic compilation. To practically realize polymorphing, we have developed an inference scheduling system, Arlo, which leverages the observed input length distribution to periodically allocate compute resources across multiple runtimes by solving an integer linear program. Upon request arrival, Arlo uses a multi-level queue-based heuristic to dispatch requests to the most suitable runtime instances, efficiently adapting to the dynamics of request length and instance load. Extensive testbed evaluations and large-scale simulations using production traces demonstrate Arlo’s promising potential. It achieves 23.7%–98.1% mean latency reductions compared to existing schemes while significantly reducing tail latency.