According to God of Prompt on Twitter, a new research paper from PayPal and NVIDIA demonstrates that significant performance improvements in agentic AI do not require massive general-purpose language models. Instead, PayPal achieved a 49% reduction in agent latency, a 58% improvement in retrieval latency, and a 45% decrease in GPU costs by replacing a slow, large LLM with a smaller, domain-specific model fine-tuned for commerce search tasks using NVIDIA’s NeMo framework. This approach, which involved targeted fine-tuning and infrastructure-grade experimentation, maintained or improved output quality. The findings highlight a shift in AI deployment strategies toward specialized small models and modular, multi-agent system architectures, providing concrete business opportunities for enterprises seeking scalable, efficient AI solutions without the overhead of large models (source: God of Prompt, Twitter; PayPal & NVIDIA research paper).