arXiv:2605.25451v1 Announce Type: new 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
The increasing complexity and scale of multimodal large language models are pushing the boundaries of current training infrastructure, necessitating more efficient architectures.
Improving the efficiency of MLLM training can significantly reduce the computational and memory costs, making advanced AI models more accessible and scalable for various applications.
This new BigMac pipeline could enable the development and deployment of more sophisticated and resource-intensive MLLMs by breaking existing efficiency trade-offs.
- · AI developers
- · Cloud providers
- · Researchers in multimodal AI
- · Industries adopting MLLMs
- · Inefficient MLLM training methods
- · Hardware vendors relying on brute-force scaling
Reduced cost and time for developing highly capable multimodal AI models.
Accelerated innovation and deployment of MLLMs across diverse sectors due to improved economic viability.
Potentially democratized access to MLLM development beyond well-funded hyperscalers, fostering broader competition and innovation.
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Read at arXiv cs.LG