Get Started
Research questionHow should partitioning, placement, and scheduling be coordinated to reduce bubbles in heterogeneous LLM training?Heterogeneous model stages can run at different speeds, leaving pipeline devices idle. Optimizing partitioning, placement, or scheduling separately can leave these bubbles unresolved.
LLM Pretraining & Post-training
Machine Learning
Latest papersRecent research connected to this question, newest first.OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and SchedulingThe source studies heterogeneous LLM training across GPU clusters, using simulation, search, and execution support for irregular schedules. Reported evidence concerns throughput across heterogeneous model configurations and cluster scales, rather than inference or other workloads.research paper · Sep 2, 2026
Related questions
How can sequence parallelism reduce communication costs for long-context LLM training across batch sizes?How can enterprises consolidate heterogeneous internal workloads onto one self-hosted LLM under data-residency constraints?How can distributed-training communication collectives adapt to external congestion in shared cloud clusters without network control?How can LLM pretraining avoid sudden gradient explosions when scaling to larger models?
Home
Topics
Search
Library