SLMs are well-positioned for the agentic era because they use a narrow slice of LLM functionality for any single language model errand. LLMs are built to be powerful generalists, but most agents use only a very narrow subset of their capabilities. They typically parse commands, generate structured outputs such as JSON for tool calls, or produce summaries and answer contextualized questions. These tasks are repetitive (up to the differences in prompt payloads), predictable, and highly specialized—well within the scope of specialized SLMs. An LLM trained to handle open-domain conversations is o
How Small Language Models Are Key to Scalable Agentic AI | NVIDIA Technical BlogComputer Vision / Video Analytics Advance Video Analytics AI Agents Using the NVIDIA AI Blueprint for Video Search and Summarization May 18, 2025 By Adam Ryason , Shubham Agrawal , Paul Shin , Prashant Gaikwad , Bhushan…
…delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents…
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…as context length increases, attention computation costs explode. Whether you’re dealing with retrieval-augmented generation (RAG) pipelines, agentic AI workflows, or long-form content generation, the \(O(N^2)\) complexity of…
Agentic AI / Generative AI How to Build a Document Processing Pipeline for RAG with Nemotron Feb 04, 2026 By Chia-Chih Chen , Moon Chung , Nave Algarici and Sean Sodha Discuss (0) Discuss…
…Project Rheo, a blueprint for a smart hospital automation and Physical AI development, combines: Physical agents : loco-manipulation and manipulation policies (for example, surgical tray pick-and-place, case cart pushing, bimanual…
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Agentic AI / Generative AI Removing the Guesswork from Disaggregated Serving AIConfigurator, an open source collaboration that works on NVIDIA Dynamo, is making multi-framework LLM deployment faster and easier. Mar 09, 2026…
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