NVIDIA Run:ai v2.24 introduces time-based fairshare, a new scheduling mode that brings fair-share scheduling with time awareness for over-quota resources to...

NVIDIA Run:ai v2.24 introduces time-based fairshare, a new scheduling mode that brings fair-share scheduling with time awareness for over-quota resources to Kubernetes clusters. This capability, built on the open source KAI Scheduler that powers NVIDIA Run:ai, addresses a long-standing challenge in shared GPU infrastructure. Consider two teams with equal priority sharing a cluster.

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In 2025, NVIDIA partnered with Black Forest Labs (BFL) to optimize the FLUX.1 text-to-image model series, unlocking FP4 image generation performance on NVIDIA...

In 2025, NVIDIA partnered with Black Forest Labs (BFL) to optimize the FLUX.1 text-to-image model series, unlocking FP4 image generation performance on NVIDIA Blackwell GeForce RTX 50 Series GPUs. As a natural extension of the latent diffusion model, FLUX.1 Kontext [dev] proved that in-context learning is a feasible technique for visual-generation models, not just large language models (LLMs).

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As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with...

As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with AI more frequently, meaning that more tokens need to be generated. To serve these tokens at the lowest possible cost, AI platforms need to deliver the best possible token throughput per watt. Through extreme co-design across GPUs, CPUs…

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Large-scale AI innovation is driving unprecedented demand for accelerated computing infrastructure. Training trillion-parameter foundation models, serving them...

Large-scale AI innovation is driving unprecedented demand for accelerated computing infrastructure. Training trillion-parameter foundation models, serving them with disaggregated architectures, and processing inference workloads at massive throughput all push data center design to the limits. To keep up, service providers need infrastructure that not only scales but also delivers stronger security…

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AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions of tokens and models scale toward...

AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions of tokens and models scale toward trillions of parameters. These systems currently rely on agentic long‑term memory for context that persists across turns, tools, and sessions so agents can build on prior reasoning instead of starting from scratch on every request.

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NVIDIA is bringing the world’s first optimized Ethernet networking with co-packaged optics to AI factories, enabling scale-out and scale-across on the NVIDIA...

NVIDIA is bringing the world’s first optimized Ethernet networking with co-packaged optics to AI factories, enabling scale-out and scale-across on the NVIDIA Rubin platform with NVIDIA Spectrum-X Ethernet Photonics, the flagship switch for multi-trillion-parameter AI infrastructure. This blog post explores key optimizations and innovations in the protocol and hardware of Spectrum-X Ethernet…

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AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI...

AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI factories that continuously convert power, silicon, and data into intelligence at scale. These factories now underpin applications that generate business plans, analyze markets, conduct deep research, and reason across vast bodies of…

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2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI...

2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI infrastructure, model optimization, open models, AI agents, and physical AI redefined how intelligent systems are trained, deployed, and moved into the real world. These posts highlight the innovations that resonated most with our readers.

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Real-time decoding is crucial to fault-tolerant quantum computers. By enabling decoders to operate with low latency concurrently with a quantum processing unit...

Real-time decoding is crucial to fault-tolerant quantum computers. By enabling decoders to operate with low latency concurrently with a quantum processing unit (QPU), we can apply corrections to the device within the coherence time. This prevents errors from accumulating, which reduces the value of results received. We can do this online, with a real quantum device, or offline…

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Data is the fuel of modern business, but relying on older CPU-based Apache Spark pipelines introduces a heavy toll. They’re inherently slow, require large...

Data is the fuel of modern business, but relying on older CPU-based Apache Spark pipelines introduces a heavy toll. They’re inherently slow, require large infrastructure, and lead to massive cloud expenditure. As a result, GPU-accelerated Spark is becoming a leading solution, providing lightning-fast performance using parallel processing. This improved efficiency reduces cloud bills and saves…

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Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They...

Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They enable “virtual manufacturing,” allowing engineers to design, build, and test transistors and integrated circuits digitally before committing to the costly physical fabrication process. This approach significantly reduces development time…

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Simulating large-scale quantum computers has become more difficult as the quality of quantum processing units (QPUs) improves. Validating the results is key to...

Simulating large-scale quantum computers has become more difficult as the quality of quantum processing units (QPUs) improves. Validating the results is key to ensure that after the devices scale beyond what is classically simulable, we can still trust the outputs. Similarly, when generating large-scale datasets for various AI models that aim to aid in the operation of quantum processors…

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NVIDIA CUDA developers have access to a wide range of tools and libraries that simplify development and deployment, enabling users to focus on the “what”...

NVIDIA CUDA developers have access to a wide range of tools and libraries that simplify development and deployment, enabling users to focus on the “what” and the “how” of their applications. An example of this is Multi-Process Service (MPS), where users can get better GPU utilization by sharing GPU resources across processes. Importantly, this can be done transparently as applications don’t…

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The AI boom reshaping the computing landscape is poised to scale even faster in 2026. As breakthroughs in model capability and computing power drive rapid...

The AI boom reshaping the computing landscape is poised to scale even faster in 2026. As breakthroughs in model capability and computing power drive rapid growth, enterprise data centers are being pushed beyond the limits of conventional server and rack architectures. This is creating new pressures on power budgets, thermal envelopes, and facility space. NVIDIA MGX modular reference…

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Today’s best AI agents rely on retrieval-augmented generation (RAG) to enable more accurate results. A RAG system facilitates the use of a knowledge base to...

Today’s best AI agents rely on retrieval-augmented generation (RAG) to enable more accurate results. A RAG system facilitates the use of a knowledge base to augment context to large language models (LLMs). A typical design pattern includes a RAG server that accepts prompt queries, consults a vector database for nearest context vectors, and then redirects the query with the appended context to an…

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Fast Fourier Transforms (FFTs) are widely used across scientific computing, from molecular dynamics and signal processing to computational fluid dynamics (CFD),...

Fast Fourier Transforms (FFTs) are widely used across scientific computing, from molecular dynamics and signal processing to computational fluid dynamics (CFD), wireless multimedia, and machine-learning applications. As computational problem sizes scale to increasingly large domains, researchers require the capability to distribute FFT computations across hundreds or thousands of GPUs spanning…

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AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter...

AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter models, and post-training—which can include fine-tuning, reinforcement learning, and other techniques—helps to further increase accuracy for specific tasks, as well as provide models with new capabilities like the ability to reason.

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As AI data centers rapidly evolve into AI factories, traditional network monitoring methods are no longer sufficient. Workloads continue to grow in complexity...

As AI data centers rapidly evolve into AI factories, traditional network monitoring methods are no longer sufficient. Workloads continue to grow in complexity and infrastructures scale rapidly, making real-time, high-frequency insights critical. The need for effective system monitoring has never been greater. This post explores how high-frequency sampling and advanced telemetry techniques…

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When using the NVIDIA Collective Communication Library (NCCL) to run a deep learning training or inference workload that uses collective operations (such as...

When using the NVIDIA Collective Communication Library (NCCL) to run a deep learning training or inference workload that uses collective operations (such as AllReduce, AllGather, and ReduceScatter), it can be challenging to determine how NCCL is performing during the actual workload run. This post introduces the NCCL Inspector Profiler Plugin, which addresses this problem. It offers a way for…

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As AI models get larger and architectures more complex, researchers and engineers are continuously finding new techniques to optimize the performance and...

As AI models get larger and architectures more complex, researchers and engineers are continuously finding new techniques to optimize the performance and overall cost of bringing AI systems to production. Model optimization is a category of techniques focused on addressing inference service efficiency. These techniques represent the best “bang for buck” opportunities to optimize cost…

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