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 Blog… As explained in this post, the latest TensorRT Edge-LLM release delivers a significant expansion in fundamental capabilities for NVIDIA DRIVE AGX Thor and NVIDIA Jetson Thor platforms. …
… 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. …
… 9 MIN READ Jan 08, 2026 Accelerating LLM and VLM Inference for Automotive and Robotics with NVIDIA TensorRT Edge-LLM Large language models LLMs and multimodal reasoning systems are rapidly expanding beyond the data center. …
… Discuss 0 Discuss 0 Tags Agentic AI / Generative AI | Developer Tools & Techniques | MLOps | General | Blackwell | TensorRT | TensorRT-LLM | Intermediate Technical | Tutorial | AI Inference | featured | Inference Performance | LLMs | PyTorch About the Authors About Lucas Liebenwein Lucas Lieben… …
… RAG enhances the capabilities of LLMs by incorporating relevant external information during output text generation. …
… Get started with emerging optimizers for LLM training Higher-order optimizers like Muon are proving essential for pushing the boundaries of LLM training efficiency. …
… The following Python commands show how to build a TensorRT-LLM engine and pass an example prompt through the model. from nemo.export.tensorrt llm import TensorRTLLM trt llm exporter = TensorRTLLM model dir=”path/to/trt llm engine” trt llm exporter.export nemo checkpoint path=”path/to/model qnemo”, … …
… Discuss 0 Discuss 0 Tags Developer Tools & Techniques | General | NeMo | TensorRT | Beginner Technical | Intermediate Technical | Tutorial | featured | LLM Techniques | LLMs | Model Optimizer About the Authors About Max Xu Max Xu is a senior technical lead at NVIDIA specializing in AI training and … …
… Now, the output is anything that the Gemma LLM can output. …