Month: July 2025

Have you ever wanted to build your own reasoning model but thought it was too complicated or required massive resources? Think again. With NVIDIA’s powerful…

Have you ever wanted to build your own reasoning model but thought it was too complicated or required massive resources? Think again. With NVIDIA’s powerful tools and datasets, you can train a small, effective reasoning model in about 48 hours, all on a single GPU. Even better, we’ve made all the code available to you to get started right away. Let’s dive in.

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The NVIDIA Collective Communications Library (NCCL) is essential for fast GPU-to-GPU communication in AI workloads, using various optimizations and tuning to…

The NVIDIA Collective Communications Library (NCCL) is essential for fast GPU-to-GPU communication in AI workloads, using various optimizations and tuning to boost performance. However, as platforms diversify, default NCCL settings may not always deliver optimal results. This post discusses why tuning is important and how users can enhance performance with custom tuner plugins. It also presents a…

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Editor’s note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots. The series also highlights the NVIDIA software and hardware powering advanced AI agents, which form the foundation of AI query engines that gather insights and perform tasks to transform
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This post explores a promising direction for building dynamic digital representations of the physical world, a topic gaining increasing attention in recent…

This post explores a promising direction for building dynamic digital representations of the physical world, a topic gaining increasing attention in recent research. We introduce an approach for constructing a digital twin in a robotic setting that stays continuously synchronized with the real world in real time. Such a twin can provide rich state information that supports and enhances a wide…

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Try the new 1T-parameter open source MoE LLM today.

Try the new 1T-parameter open source MoE LLM today.

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Ever relied on an old GPS that didn’t know about the new highway bypass, or a sudden road closure? It might get you to your destination, but not in the most…

Ever relied on an old GPS that didn’t know about the new highway bypass, or a sudden road closure? It might get you to your destination, but not in the most efficient or accurate way. AI agents face a similar challenge: they often rely on static training data. This data is fixed at a point in time—while it was current when created, it can quickly become outdated. This limitation can cause…

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At its core, NVIDIA Air is built for automation. Every part of your network can be coded, versioned, and set to trigger automatically. This includes creating…

At its core, NVIDIA Air is built for automation. Every part of your network can be coded, versioned, and set to trigger automatically. This includes creating the topology, configuring the network, and validating its setup. Automation reduces manual error, speeds up testing, and brings the same rigor to networking that modern DevOps teams apply to software development. Let’s discuss the basic…

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Running inference with large language models (LLMs) in production requires meeting stringent latency constraints. A critical stage in the process is LLM decode,…

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If you work with pandas, you’ve probably hit the wall. It’s that moment when your trusty workflow, so elegant on smaller datasets, grinds to a halt on a…

If you work with pandas, you’ve probably hit the wall. It’s that moment when your trusty workflow, so elegant on smaller datasets, grinds to a halt on a large one. A script that once took seconds now crawls for minutes. Your next steps are predictable and frustrating. You might downsample your data and lose fidelity, rewrite your logic to process data in chunks, or face the daunting task of…

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