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- Small Language Models are the Future of Agentic AI
Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI
- 《Small Language Models are the Future of Agentic AI》翻译与解读
最近， NVIDIA 团队发布了一篇技术报告，讨论了 小语言模型 （Small Language Model， SML）是未来 Agentic AI 的发展趋势。 文章主要由作者们提出的支持 SLM 是未来趋势的观点、一些不同的观点以及作者们给出的辩驳论点组成，涉及对未来发展趋势的分析与预测。
- (PDF) Small Language Models are the Future of Agentic AI
We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm
- 论文介绍：《Small Language Models are the Future of Agentic AI》
《Small Language Models are the Future of Agentic AI》是一篇具有强烈实践导向和行业洞察的论文。 它不仅系统论证了SLMs在智能体系统中的优势，还提供了可行的迁移路径和应对反对意见的逻辑框架。
- How Small Language Models Are Key to Scalable Agentic AI
Titled Small Language Models are the Future of Agentic AI, we highlight the growing opportunities for integrating SLMs in place of LLMs in agentic applications, decreasing costs, and increasing operational flexibility
- Thinking Small: Small Language Models Could Reshape Agentic AI
The study, Small Language Models are the Future of Agentic AI, contends that large language models (LLMs) are overused in agentic AI — or systems in which software agents complete tasks autonomously by invoking tools, reasoning about steps, and interacting with users
- Small Language Models: The Breakthrough Force Behind Future of Agentic AI
Explore how Small Language Models (SLMs) are revolutionizing agentic AI—efficient, scalable, and ready for the real world Powered by NVIDIA insights
- The Rise of Small Language Models in Agentic AI - neon. ai
Drawing from a comprehensive whitepaper by NVIDIA Research, the article argues that models with fewer than 10 billion parameters are not just adequate, but often preferable for many agentic applications—systems that act autonomously, interact with tools or APIs, and automate tasks
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