PipelinedLLEP: Managing Memory Peaks in Long-Context MoE Training
September 12, 2026
PipelinedLLEP bounds the four primary memory drivers in MoE training—expert dispatch, vocabulary projection, gradient checkpoint boundaries, and optimizer states—to prevent OOM errors. It uses a schedule to keep the GPU working set fixed at launch regardless of context length.
HOW THIS AFFECTS YOU
●
builderThis provides a strategy for scaling training infrastructure without encountering unpredictable memory spikes.
●
researcherYou can train larger MoE models at longer contexts by managing peak allocations rather than average footprints.