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arXiv · 2509.00217

Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference

Ruokai Yin·Sattwik Deb Mishra·Xuan Zuo·Hokchhay Tann·Preyas Shah·Apala Guha
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Abstract

Distributed LLM inference requires careful coordination of parallelization strategies across hundreds to thousands of NPUs to meet production SLOs. Current systems like Megatron-LM rely on static heuristics that separately configure parallelism degrees and per-operator sharding dimensions, leaving significant performance on the table as models scale and hardware topologies diversify. We introduce Learn to Shard, to our knowledge, the first RL-based approach to co-optimize both coarse-grained parallelism degrees and fine-grained per-operator sharding dimensions for distributed LLM inference. Our method employs an attention-based policy over an elite history that learns from high-performing strategies to efficiently navigate the vast combinatorial search space. Evaluated on H100 clusters with MoE models up to 1.6T parameters, Learn to Shard achieves up to 3.5x throughput improvement over metaheuristic baselines and 1.06x over Megatron heuristics.

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Ruokai Yin, Sattwik Deb Mishra, Xuan Zuo, Hokchhay Tann, Preyas Shah, Apala Guha. 2025-08-29. Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference. https://arxiv.org/abs/2509.00217

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