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…This post explores the latest Cosmos WFMs, their key capabilities that advance physical AI , and how to use them. Cosmos world foundation model updates: NVIDIA Cosmos world foundation models have continued to…
…Returned context is inserted into the enrichment prompt set in the tunable VECTOR_RAG_ENRICHMENT_PROMPT before LLM generation. The tunable enrichment prompt used in the nutritional example is pictured below. Here…
…TensorRT for RTX is only compatible with NVIDIA RTX GPUs, from the Turing generation (compute capability 7.5) up to the NVIDIA Blackwell generation (compute capability 10.0). Unreal Engine neural network…
…While modern GPUs boast impressive compute capabilities, their performance is frequently limited by how quickly data can be moved between different devices: CPU memory to GPU memory GPU memory to CPU memory…
…It uses accelerated vision-based microservices, vision-language models (VLMs) , large language models (LLMs) , and retrievers for real-time video intelligence, agentic search, and automated reporting. VSS helps enterprises monitor operations, detect…
…Once installed, the agent harness sees a single deep research capability. Phrases like “research the regulatory landscape for X across our internal policy docs and produce a memo” route through the skill…
…Architecture overview and data flow Extraction pipeline Nemotron Parse is integrated into the extraction pipeline, replacing previous OCR-based solutions and extending visual extraction capabilities to handle complex document structures such as…
…The MiniMax M2 series is a sparse mixture-of-experts (MoE) model family designed for efficiency and capability. The MoE design keeps inference costs low while preserving the full capacity of a…
…Such malicious instructions to the LLM can result in it taking attacker-influenced actions with adverse consequences. Manual approval of actions performed by the agent is the most common way to manage…
…Because the tokens enabling these capabilities are inherently expensive to process, delivered performance stands as the critical lever to making these systems both scalable and profitable. Driving down the cost of these…