In September, NVIDIA published an article outlining the value of custom SLMs for Agentic AI working on-prem on machines like HP Z workstations.Key Takeaways:1. Economics of Small Agentic Models (SLMs):Small Language Models (under ~10B parameters) can replace generalist LLMs for most agentic tasks. They’re 10–30× cheaper to serve, require fewer GPUs, and can be fine-tuned in hours instead of weeks. This shift lowers operational costs, reduces latency, and improves sustainability—critical as AI agents scale across industries.2. Localization and Democratization:SLMs enable local, domain-specific adaptation—they can be easily fine-tuned to meet regional compliance, cultural norms, or specialized workflows. This encourages on-prem and edge deployment, supporting sovereign data control and fostering diversity by allowing more organizations to build their own tailored agents.3. On-Prem Model Creation and Management:With frameworks like NVIDIA Dynamo and ChatRTX, real-time, offline inference b
