DataBloom

As AI workloads scale, fast and reliable GPU communication becomes vital, not just for training, but increasingly for inference at scale. The NVIDIA Collective…

As AI workloads scale, fast and reliable GPU communication becomes vital, not just for training, but increasingly for inference at scale. The NVIDIA Collective Communications Library (NCCL) delivers high-performance, topology-aware collective operations: , , , , and optimized for NVIDIA GPUs and a variety of interconnects including PCIe, NVLink, Ethernet (RoCE), and InfiniBand (IB).

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Discover leaderboard-winning RAG techniques, integration strategies, and deployment best practices.

Discover leaderboard-winning RAG techniques, integration strategies, and deployment best practices.

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As the scale of AI training increases, a single data center (DC) is not sufficient to deliver the required computational power. Most recent approaches to…

As the scale of AI training increases, a single data center (DC) is not sufficient to deliver the required computational power. Most recent approaches to address this challenge rely on multiple data centers being co-located or geographically distributed. In a recently open-sourced feature, the NVIDIA Collective Communication Library (NCCL) is now able to communicate across multiple data centers…

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NVDIA Run:ai 2.22 is now here. It brings advanced inference capabilities, smarter workload management, and more controls.

NVDIA Run:ai 2.22 is now here. It brings advanced inference capabilities, smarter workload management, and more controls.

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While speech AI is used to build digital assistants and voice agents, its impact extends far beyond these applications. Core technologies like text-to-speech…

While speech AI is used to build digital assistants and voice agents, its impact extends far beyond these applications. Core technologies like text-to-speech (TTS) and automatic speech recognition (ASR) are driving innovation across industries. They’re enabling real-time translation, powering interactive digital humans, and even helping restore speech for individuals who’ve lost their voices.

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Being able to predict extreme weather events is essential as such conditions become more common and destructive. Subseasonal climate forecasting—predicting…

Being able to predict extreme weather events is essential as such conditions become more common and destructive. Subseasonal climate forecasting—predicting weather two or more weeks in the future—underpins proactive decision making and risk management across sectors that are sensitive to weather fluctuations. It can help farmers better choose which crops to grow and manage their water…

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Human action recognition is a capability in AI systems designed for safety-critical applications, such as surveillance, eldercare, and industrial monitoring….

Human action recognition is a capability in AI systems designed for safety-critical applications, such as surveillance, eldercare, and industrial monitoring. However, many real-world datasets are limited by data imbalance, privacy constraints, or insufficient coverage of rare but important actions such as falls or accidents. In this post, I present SynthDa, a modular synthetic data augmentation…

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Ceramics — the humble mix of earth, fire and artistry — have been part of a global conversation for millennia. From Tang Dynasty trade routes to Renaissance palaces, from museum vitrines to high-stakes auction floors, they’ve carried culture across borders, evolving into status symbols, commodities and pieces of contested history. Their value has been shaped
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As one of the world’s largest emerging markets, Indonesia is making strides toward its “Golden 2045 Vision” — an initiative tapping digital technologies and bringing together government, enterprises, startups and higher education to enhance productivity, efficiency and innovation across industries. Building out the nation’s AI infrastructure is a crucial part of this plan. That’s why
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Today’s most widely used climate simulators often struggle: They can’t fully capture critical small-scale processes, like thunderstorms or towering tropical clouds, because of computational limits. To capture these features, scientists run ultra-high-resolution simulations called cloud-resolving models (CRMs). These simulations track how clouds form and evolve—but they’re so expensive, running one for a decade of global climate forecasts is practically impossible. What if we could distill the wisdom of these detailed simulations into a machine learning model that runs tens to hundreds of times faster?
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