Towards Pareto Optimal Throughput in Small Language Model Serving

• URL: http://arxiv.org/abs/2404.03353v1
• Date: Thu, 4 Apr 2024 10:45:07 GMT
• Title: Towards Pareto Optimal Throughput in Small Language Model Serving
• Authors: Pol G. Recasens, Yue Zhu, Chen Wang, Eun Kyung Lee, Olivier Tardieu, Alaa Youssef, Jordi Torres, Josep Ll. Berral,
• Abstract summary: Small Language Models (SLMs) offer new opportunities for resource-constrained users. We present a set of experiments designed to benchmark SLM inference at performance and energy levels.
• Score: 4.497936996651617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks. Although serving LLMs is computationally and memory demanding, the rise of Small Language Models (SLMs) offers new opportunities for resource-constrained users, who now are able to serve small models with cutting-edge performance. In this paper, we present a set of experiments designed to benchmark SLM inference at performance and energy levels. Our analysis provides a new perspective in serving, highlighting that the small memory footprint of SLMs allows for reaching the Pareto-optimal throughput within the resource capacity of a single accelerator. In this regard, we present an initial set of findings demonstrating how model replication can effectively improve resource utilization for serving SLMs.

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