What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands?...

What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands? In Part 1 of our series on building a computer use agent, we built a custom Bash computer-use agent using NVIDIA Nemotron in just one hour. In this sequel, we’ll take it further by teaching the same reasoning model with no prior…

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For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: as context length increases, attention computation costs...

For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: 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 complexity of attention remains a primary bottleneck. This post explains a technique known as…

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AI models are becoming increasingly complex, often exceeding the capabilities of available hardware. Quantization has emerged as a crucial technique to address...

AI models are becoming increasingly complex, often exceeding the capabilities of available hardware. Quantization has emerged as a crucial technique to address this challenge, enabling resource-intensive models to run on constrained hardware. The NVIDIA TensorRT and Model Optimizer tools simplify the quantization process, maintaining model accuracy while improving efficiency.

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Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and...

Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and statistical expertise. Managing feature engineering, model tuning, and consistency across workflows is complex and error-prone. These challenges are amplified by the slow, sequential nature of CPU-based ML workflows…

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Training massive mixture-of-experts (MoE) models has long been the domain of a few advanced users with deep infrastructure and distributed-systems expertise....

Training massive mixture-of-experts (MoE) models has long been the domain of a few advanced users with deep infrastructure and distributed-systems expertise. For most developers, the challenge wasn’t building smarter models—it was scaling them efficiently across hundreds or even thousands of GPUs without breaking the bank. With NVIDIA NeMo Automodel, an open-source library within NVIDIA NeMo…

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