Safeguarding AI agents and other conversational AI applications to ensure safe, on-brand and reliable behavior is essential for enterprises. NVIDIA NeMo Guardrails offers robust protection with AI guardrails for content safety, topic control, jailbreak detection, and more to evaluate and optimize guardrail performance. In this post, we explore techniques for measuring and optimizing your AI…
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AI agents are transforming business operations by automating processes, optimizing decision-making, and streamlining actions. Their effectiveness hinges on expert reasoning, enabling smarter planning and efficient execution. Agentic AI applications could benefit from the capabilities of models such as DeepSeek-R1. Built for solving problems that require advanced AI reasoning…
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Large language models (LLMs) have permeated every industry and changed the potential of technology. However, due to their massive size they are not practical for the current resource constraints that many companies have. The rise of small language models (SLMs) bridge quality and cost by creating models with a smaller resource footprint. SLMs are a subset of language models that tend to…
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Vision language models (VLMs) are evolving at a breakneck speed. In 2020, the first VLMs revolutionized the generative AI landscape by bringing visual understanding to large language models (LLMs) through the use of a vision encoder. These initial VLMs were limited in their abilities, only able to understand text and single image inputs. Fast-forward a few years and VLMs are now capable of…
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Chip and hardware design presents numerous challenges stemming from its complexity and advancing technologies. These challenges result in longer turn-around time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops. Large language models (LLMs) have shown a remarkable capacity to comprehend and generate natural…
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Explore visually perceptive AI agents, the latest vision AI technologies, hands-on training, and inspiring deployments.
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As AI models extend their capabilities to solve more sophisticated challenges, a new scaling law known as test-time scaling or inference-time scaling is emerging. Also known as AI reasoning or long-thinking, this technique improves model performance by allocating additional computational resources during inference to evaluate multiple possible outcomes and then selecting the best one…
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At NVIDIA, the Sales Operations team equips the Sales team with the tools and resources needed to bring cutting-edge hardware and software to market. Managing this across NVIDIA’s diverse technology is a complex challenge shared by many enterprises. Through collaboration with our Sales team, we found that they rely on internal and external documentation…
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AI agents present a significant opportunity for businesses to scale and elevate customer service and support interactions. By automating routine inquiries and enhancing response times, these agents improve efficiency and customer satisfaction, helping organizations stay competitive. However, alongside these benefits, AI agents come with risks. Large language models (LLMs) are vulnerable to…
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This post was originally published July 29, 2024 but has been extensively revised with NVIDIA AI Blueprint information. Traditional video analytics applications and their development workflow are typically built on fixed-function, limited models that are designed to detect and identify only a select set of predefined objects. With generative AI, NVIDIA NIM microservices…
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Agentic AI, the next wave of generative AI, is a paradigm shift with the potential to revolutionize industries by enabling AI systems to act autonomously and achieve complex goals. Agentic AI combines the power of large language models (LLMs) with advanced reasoning and planning capabilities, opening a world of possibilities across industries, from healthcare and finance to manufacturing and…
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Agentic AI workflows often involve the execution of large language model (LLM)-generated code to perform tasks like creating data visualizations. However, this code should be sanitized and executed in a safe environment to mitigate risks from prompt injection and errors in the returned code. Sanitizing Python with regular expressions and restricted runtimes is insufficient…
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AI agents powered by large language models (LLMs) help organizations streamline and reduce manual workloads. These agents use multilevel, iterative reasoning to analyze problems, devise solutions, and execute tasks with various tools. Unlike traditional chatbots, LLM-powered agents automate complex tasks by effectively understanding and processing information. To avoid potential risks in specific…
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When interfacing with generative AI applications, users have multiple communication options—text, voice, or through digital avatars. Traditional chatbot or copilot applications have text interfaces where users type in queries and receive text-based responses. For hands-free communication, speech AI technologies like automatic speech recognition (ASR) and text-to-speech (TTS) facilitate…
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The exponential growth of visual data—ranging from images to PDFs to streaming videos—has made manual review and analysis virtually impossible. Organizations are struggling to transform this data into actionable insights at scale, leading to missed opportunities and increased risks. To solve this challenge, vision-language models (VLMs) are emerging as powerful tools…
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AI agents are emerging as the newest way for organizations to increase efficiency, improve productivity, and accelerate innovation. These agents are more advanced than prior AI applications, with the ability to autonomously reason through tasks, call out to other tools, and incorporate both enterprise data and employee knowledge to produce valuable business outcomes. They’re being embedded into…
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For any data center, operating large, complex GPU clusters is not for the faint of heart! There is a tremendous amount of complexity. Cooling, power, networking, and even such benign things like fan replacement cycles all must be managed effectively and governed well in accelerated computing data centers. Managing all of this requires an accelerated understanding of the petabytes of telemetry data…
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Immerse yourself in NVIDIA technology with our full-day, hands-on technical workshops at our AI Summit in Washington D.C. on October 7, 2024.
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Learn how to build high-performance solutions with NVIDIA visual AI agents that help streamline operations across a range of industries.
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NVIDIA NIM, part of NVIDIA AI Enterprise, now supports tool-calling for models like Llama 3.1. It also integrates with LangChain to provide you with a production-ready solution for building agentic workflows. NIM microservices provide the best performance for open-source models such as Llama 3.1 and are available to test for free from NVIDIA API Catalog in LangChain applications.
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