Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs

arXiv — cs.LG•Wednesday, November 5, 2025 at 5:00:00 AM

The article addresses the significant challenge of scaling large language models (LLMs) efficiently across multiple GPUs, a critical issue in distributed machine learning. To tackle this, the authors introduce an analytical framework termed the "Three Taxes," designed to identify and quantify performance inefficiencies that arise during multi-GPU execution. By systematically analyzing these performance penalties, the framework aims to guide improvements in distributed execution strategies. The ultimate goal of this approach is to eliminate the so-called "performance taxes" that degrade efficiency, thereby enhancing the scalability and speed of LLM training and inference. This systems-level perspective offers a structured method to optimize resource utilization in multi-GPU environments. The framework and its objectives align with ongoing research efforts focused on improving distributed machine learning performance. Overall, the article contributes to the broader discourse on making large-scale AI models more computationally efficient and practical for real-world applications.

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