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This work provides an exact critical-path characterization of the makespan for micro-batch pipelines serving large language models on heterogeneous GPU clusters, correcting errors in the classic homogeneous GPipe formula. It quantifies the impact of stage service times and micro-batch counts on inf...
This work provides an exact critical-path characterization of the makespan for micro-batch pipelines serving large language models on heterogeneous GPU clusters, correcting errors in the classic homogeneous GPipe formula. It quantifies the impact of stage service times and micro-batch counts on inf...
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We give an exact critical-path characterization of the makespan of micro-batch pipelines serving LLMs on heterogeneous GPU clusters, quantifying how the classic homogeneous GPipe formula errs. In the pure-forward, communication-free, infinite-buffer setting, T(M) = (M-1)tau_max + Sum_j tau_j, where tau_j is stage...
Understanding precise makespan bounds helps optimize LLM inference pipelines on heterogeneous hardware, improving efficiency and resource allocation. This insight addresses limitations of existing models that assume homogeneous processing stages.
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