英伟达新研究：小模型才是智能体的未来？

Core Viewpoint - The article emphasizes that small language models are the future of Agentic AI, as they are more efficient and cost-effective compared to large models, which often waste resources on simple tasks [3][4][40]. Summary by Sections Performance Comparison - Small models can outperform large models in specific tasks, as evidenced by a 6.7 billion parameter Toolformer surpassing the performance of the 175 billion parameter GPT-3 [6]. - A 7 billion parameter DeepSeek-R1-Distill model has also shown better performance than Claude3.5 and GPT-4o [7]. Resource Optimization - Small models optimize hardware resources and task design, allowing for more efficient execution of Agent tasks [9]. - They can efficiently share GPU resources, maintain performance isolation, and reduce memory usage, enhancing concurrent capabilities [11][12]. - Flexible GPU resource allocation allows for better overall throughput and cost control by prioritizing low-latency requests from small models [14]. Task-Specific Deployment - Traditional Agent tasks often do not require a single large model; instead, specialized small models can be used for specific sub-tasks, reducing resource waste and inference costs [20][23]. - Running a 7 billion parameter small model is 10-30 times cheaper than using a 700-1750 billion parameter large model [24]. Challenges and Counterarguments - Some researchers argue that large models have superior general understanding capabilities, even in specialized tasks [26]. - However, NVIDIA counters that small models can achieve the required reliability through easy fine-tuning and that advanced systems can break down complex problems into simpler sub-tasks, diminishing the importance of large models' generalization [27][28]. Economic Considerations - While small models have lower per-inference costs, large models may benefit from economies of scale in large deployments [30]. - NVIDIA acknowledges this but points out that advancements in inference scheduling and modular systems are improving the flexibility and reducing infrastructure costs for small models [31]. Transitioning from Large to Small Models - NVIDIA outlines a method for transitioning from large to small models, including adapting infrastructure, increasing market awareness, and establishing evaluation standards [33]. - The process involves data collection, workload clustering, model selection, fine-tuning, and creating a feedback loop for continuous improvement [36][39]. Community Discussion - The article highlights community discussions around the practicality of small models versus large models, with some users finding small models more cost-effective for simple tasks [41]. - However, concerns about the robustness of small models in unpredictable scenarios are also raised, suggesting a need for careful consideration of the trade-offs between functionality and complexity [43][46].