RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.
Xpipe: Efficient pipeline model parallelism for multi-gpu dnn training.arXiv preprint arXiv:1911.04610, 2019
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One-step gradient delay is optimizer-dependent rather than intrinsically unstable, with Muon and error-feedback correction enabling async pipeline parallelism to match synchronous performance on models up to 10B parameters.
PACI enables bubble-free asynchronous pipeline training by bounding version drift via local gradient accumulation, matching synchronous stability with higher throughput and no extra memory.
citing papers explorer
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Efficient Training on Multiple Consumer GPUs with RoundPipe
RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.
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One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining
One-step gradient delay is optimizer-dependent rather than intrinsically unstable, with Muon and error-feedback correction enabling async pipeline parallelism to match synchronous performance on models up to 10B parameters.
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Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
PACI enables bubble-free asynchronous pipeline training by bounding version drift via local gradient accumulation, matching synchronous stability with higher throughput and no extra memory.