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BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
[Submitted on 25 May 2026 (v1), last revised 7 Jul 2026 (this ve · 2026-05-26 · via cs.LG updates on arXiv.org

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Abstract:Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, but remain bound by a Pareto frontier between compute and memory efficiency, improving one only at the expense of the other. We present BigMac, a new training pipeline for multimodal LLMs. The core idea of BigMac is to elegantly nest the encoder and generator computation into the original LLM pipeline, forming a dependency-safe nested pipeline structure. With this design, BigMac reduces the activation memory complexity of the encoder and generator to O(1) while keeping the activation memory complexity of the LLM unchanged. At the same time, it achieves the same computational efficiency as the idealized setting with unlimited memory. As a result, BigMac breaks the Pareto frontier between computational efficiency and memory usage, enabling simultaneous optimization of both computation and memory in MLLM training. We evaluate BigMac on multiple MLLMs and training workloads. Experimental results show that BigMac achieves a 1.08$\times$-1.9$\times$ training speedup over baseline systems while maintaining stable memory usage as batch size increases.

Submission history

From: Zili Zhang [view email]
[v1] Mon, 25 May 2026 06:01:45 UTC (5,108 KB) (withdrawn)
[v2] Fri, 5 Jun 2026 07:45:26 UTC (5,108 KB)
[v3] Tue, 7 Jul 2026 02:29:51 UTC (4,972 KB)

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