Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference
This addresses performance bottlenecks in distributed LLM inference for production systems, offering a novel RL-based approach that is incremental over existing heuristics.
The paper tackles the problem of optimizing distributed LLM inference by co-optimizing parallelism degrees and per-operator sharding dimensions, achieving up to 3.5x throughput improvement over metaheuristic baselines and 1.06x over Megatron heuristics on H100 clusters with MoE models up to 1.6T parameters.
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.