In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including decode, preprocessing, and GPU scheduling. In the previous post, Build High-Performance Vision AI Pipelines with NVIDIA CUDA-Accelerated VC-6, this was described as the data-to-tensor gap—a performance mismatch between AI pipeline stages. The SMPTE VC-6 (ST 2117-1) codec…
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The Gemmaverse expands with the launch of the latest Gemma 4 multimodal and multilingual models, designed to scale across the full spectrum of deployments, from NVIDIA Blackwell in the data center to Jetson at the edge. These models are suited to meet the growing demand for local deployment for AI development and prototyping, secure on-prem requirements, cost efficiency, and latency-sensitive use…
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In algorithmic trading, reducing response times to market events is crucial. To keep pace with high-speed electronic markets, latency-sensitive firms often use specialized hardware like FPGAs and ASICs. Yet, as markets grow more efficient, traders increasingly depend on advanced models such as deep neural networks to enhance profitability. Because implementing these complex models on low-level…
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Note: CUDA Tile Programming in BASIC is an April Fools’ joke, but it’s also real and actually works, demonstrating the flexibility of CUDA. CUDA 13.1 introduced CUDA Tile, a next generation tile-based GPU programming paradigm designed to make fine-grained parallelism more accessible and flexible. One of its key strengths is language openness: any programming language can target CUDA Tile…
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Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications. Rigorous AI inference performance benchmarks are critical to understanding real-world token output, which drives AI factory revenue. MLPerf Inference v6.0 is the latest in a series of industry benchmarks that measure…
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In today’s AI factory environment, performance is not theoretical. It is economic, competitive, and existential. A 1% drop in usable GPU time can mean millions of tokens lost per hour. Minutes of congestion can cascade into hours of recovery. A rack-level power oversubscription can lead to stranded power and reduced tokens per watt, silently eroding factory output at scale. As AI factories scale…
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Spatial computing is moving from visualization to active collaboration, adding increasingly more GPU demands on XR hardware to render photorealistic, physics-accurate, high-fidelity spatial content in real time. Meanwhile, developers have had to maintain separate codebases for every platform, each with different toolchains, SDKs, and streaming protocols. At NVIDIA GTC 2026, NVIDIA CloudXR 6.0…
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Delivering high-fidelity VR and AR experiences to enterprise users has typically required native application development, custom device management, and complex deployment pipelines. Now, with the new JavaScript SDK NVIDIA CloudXR.js, developers can stream GPU-rendered immersive content directly to a standard web browser—no app store, no installs, no device-specific builds. NVIDIA CloudXR.
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In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more GPUs and can’t easily share across GPUs across models…
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In the current state of automotive radar, machine learning engineers can’t work with camera-equivalent raw RGB images. Instead, they work with the output of radar constant false alarm rate (CFAR), which is similar to computer vision (CV) edge detections. The communications and compute architectures haven’t kept pace with trends in AI and the needs of Level 4 autonomy, despite radar being a staple…
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Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or small molecule. The search space for possible amino acid sequence permutations and resulting 3D protein structures for a designed binder is vast, and achieving strong, specific binding requires careful optimization of the interactions between…
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In the AI era, power is the ultimate constraint, and every AI factory operates within a hard limit. This makes performance per watt—the rate at which power is converted into revenue-generating intelligence—the defining metric for modern AI infrastructure. AI data centers now operate as token factories tied directly to the energy ecosystem, where access to land, power…
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Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale, developers need models that can understand real-world multimodal data, converse naturally with users globally, and operate safely across languages and modalities. At GTC 2026, NVIDIA introduced a new generation of NVIDIA Nemotron models…
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Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime management. From factory automation cells to autonomous mobile platforms to surgical rooms, operators are deploying increasingly complex generative AI models, more sensors, and higher‑fidelity data streams at the edge.
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AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud. This includes sensitive information like patient records, market research, and legacy systems containing enterprise knowledge. There’s also a risk of using private data with AI models, and adoption is often slowed or blocked by privacy and trust concerns.
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As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages have fundamentally different compute profiles, yet traditional deployments force them onto the same hardware, leaving GPUs underutilized and scaling inflexible. Disaggregated serving addresses this by splitting the inference pipeline…
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While consumer AI offers powerful capabilities, workplace tools often suffer from disjointed data and limited context. Built with LangChain, the NVIDIA AI-Q blueprint is an open source template that bridges this gap. LangChain recently introduced an enterprise agent platform built with NVIDIA AI to support scalable, production-ready agent development. This tutorial, available as an NVIDIA…
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AI-native services are exposing a new bottleneck in AI infrastructure: As millions of users, agents, and devices demand access to intelligence, the challenge is shifting from peak training throughput to delivering deterministic inference at scale—predictable latency, jitter, and sustainable token economics. NVIDIA announced at GTC 2026 that telcos and distributed cloud providers are…
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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 trillions of parameters. These systems 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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Reasoning models are growing rapidly in size and are increasingly being integrated into agentic AI workflows that interact with other models and external tools. Deploying these models and workflows in production environments requires distributing them across multiple GPU nodes, which demands careful orchestration and coordination across GPUs. NVIDIA Dynamo 1.0—available now—addresses these…
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Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that use multiple communication channels and background subprocesses simultaneously to explore options, test solutions, and generate optimal results. This places extreme demands on local compute. NVIDIA DGX Spark provides the performance necessary for autonomous agents to execute…
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Building AI factories is complex and requires efficient integration across compute, networking, security, and storage systems. To achieve rapid Time to AI and strong ROI, the new NVIDIA DSX Air is enabling organizations to simulate their entire AI factory infrastructure in the cloud—covering compute, networking, storage, and security. Being able to design, test, and optimize systems before…
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AI is evolving, and reasoning models are increasing token demand, placing new requirements on every layer of AI infrastructure. More than ever, compute must scale efficiently to maximize token production and improve productivity for model creators and users. Modern GPUs operate at peak capacity, pushing throughput higher every generation, but system performance is increasingly gated by the…
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AI has evolved from assistants following your directions to agents that act independently. Called claws, these agents can take a goal, figure out how to achieve it, and execute indefinitely—while leaving you out of the loop. The more capable claws become, the harder they are to trust. And their self-evolving autonomy changes everything about the environment in which they operate.
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NVIDIA Groq 3 LPX is a new rack-scale inference accelerator for the NVIDIA Vera Rubin platform, designed for the low-latency and large-context demands of agentic systems. Co-designed with the NVIDIA Vera Rubin NVL72, LPX equips the AI factory with an engine optimized for fast, predictable token generation, while Vera Rubin NVL72 remains the flexible, general-purpose workhorse for training and…
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Artificial intelligence is token-driven. Every prompt, reasoning step, and agent interaction generates tokens. Over the past year, token consumption has grown multifold and now exceeds 10 quadrillion tokens per year. And while the majority of tokens have been generated from humans interacting with AI, the new era is one in which most tokens will be generated from AI interacting with AI.
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Physics forms the foundation of robotic simulation, enabling realistic modeling of motion and interaction. For tasks like locomotion and manipulation, simulators must handle complex dynamics such as contact forces and deformable objects. While most engines trade off speed for realism, Newton—a GPU-accelerated, open source simulator—is designed to do both. Newton 1.0 GA…
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The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don’t get proper training and face testing risks due to poor generalization, limited exposure to real-world variations, and unpredictable behavior in edge cases. Collecting massive real-world datasets for…
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Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across geometries and operating conditions. Unlike LLMs, these models depend on large volumes of high-fidelity, physics-compliant data. Recent scaling-law work on computational fluid dynamics (CFD) surrogates indicates that simulation-generated…
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Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and workload configurations. You get one cluster working, and spend days getting the next one to match. Upgrade a component, and something else breaks. Move to a new cloud and start over. AI Cluster Runtime is a new open-source project designed…
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Physical AI is rapidly evolving, from next-generation software-defined autonomous vehicles (AVs) to humanoid robots. The challenge is no longer how to run a large language model (LLM), but how to enable high-fidelity reasoning, real-time multimodal interaction, and trajectory planning within strict power and latency envelopes. NVIDIA TensorRT Edge-LLM, a high-performance C++ inference runtime…
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Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and long-context analysis, while remaining efficient enough to run continuously at scale. Multi-agent systems generate up to 15x the tokens of standard chats, re-sending history, tool outputs, and reasoning steps at every turn. Over long tasks…
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NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new standard for visuals and performance. At GDC 2026, NVIDIA unveiled the latest path tracing innovations elevating visual fidelity, on-device AI models enabling players to interact with their favorite experiences in new ways, and enterprise solutions accelerating game development…
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Agentic code assistants are moving into daily game development as studios build larger worlds, ship more DLCs, and support distributed teams. These assistants can accelerate development by helping with tasks like generating gameplay scaffolding, refactoring repetitive systems, and answering engine-specific questions faster. This post outlines how developers can build reliable AI coding…
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CUDA 13.2 arrives with a major update: NVIDIA CUDA Tile is now supported on devices of compute capability 8.X architectures (NVIDIA Ampere and NVIDIA Ada), as well as 10.X, 11.X and 12.X architectures (NVIDIA Blackwell). In an upcoming release of the CUDA Toolkit, all GPU architectures starting with Ampere will be fully supported. If you’re using Ampere, Ada, or Blackwell GPU architectures…
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In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the foundational framework for training massive transformer models at scale. The open source library offers industry-leading parallelism and GPU-optimized performance. Now developed GitHub-first in the NVIDIA/Megatron-LM repo, Megatron Core is increasingly shaped by contributions from…
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Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and nodes to scale to more users while reducing latency. Distributed inference frameworks use techniques such as disaggregated serving, KV cache loading, and wide expert parallelism. In disaggregated serving environments…
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Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal configuration for any given workload (such as hardware, parallelism, and prefill/decode split) resides in a massive, multi-dimensional search space that is impossible to explore manually or through exhaustive testing. AIConfigurator…
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In this post, we dive into one of the most critical workloads in modern AI: Flash Attention, where you’ll learn: Environment requirements: See the quickstart doc for more information on installing cuTile Python. The attention mechanism is the computational heart of transformer models. Given a sequence of tokens, attention enables each token to “look at” every other…
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NVIDIA ACE is a suite of technologies for building AI agents for gaming. ACE provides ready-to-integrate cloud and on-device AI models for every part of in-game characters, from speech to intelligence to animation. To run these models alongside the game engine efficiently, the NVIDIA In-Game Inferencing (NVIGI) SDK includes a set of performant libraries that developers can integrate into C++…
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NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to tensor cores and other specialized hardware. Earlier this year, NVIDIA released cuTile for Python, giving Python developers a natural way to write high-performance GPU kernels. Now, the same programming model is available in Julia through cuTile.jl. In this blog post…
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Autonomous networks are quickly becoming one of the top priorities in telecommunications. According to the latest NVIDIA State of AI in Telecommunications report, 65% of operators said AI is driving network automation, and 50% named autonomous networks as the top AI use case for ROI. Yet many telcos still report gaps in AI and data science expertise. This makes it difficult to scale safe…
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To make 6G a reality, the telecom industry must overcome a fundamental challenge: how to design, train, and validate AI-native networks that are too complex to be tested in the physical world. The NVIDIA Aerial Omniverse Digital Twin (AODT) solves this by enabling a continuous integration/continuous development (CI/CD)-style workflow where Radio Access Network (RAN) software is trained…
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Alibaba has introduced the new open source Qwen3.5 series built for native multimodal agents. The first model in this series is a ~400B parameter native vision-language model (VLM) with reasoning built with a hybrid architecture of mixture of experts (MoE) and Gated Delta Networks. Qwen3.5 can understand and navigate user interfaces, which improves on the previous generation of VLMs. Qwen3.5…
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Organizations deploying LLMs are challenged by inference workloads with different resource requirements. A small embedding model might use only a few gigabytes of GPU memory, while a 70B+ parameter LLM could require multiple GPUs. This diversity often leads to low average GPU utilization, high compute costs, and unpredictable latency. The problem isn’t just about packing more workloads onto…
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LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent Attention (MLA) and Grouped Query Attention (GQA). As a result, AI ”speed of thought” is increasingly governed not by the massive throughput of matrix multiplications, but by the transcendental math of the softmax function. Transcendentals refer to functions that cannot be…
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As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as training throughput expectations, memory limits, and rising costs are becoming the primary barriers to scaling transformer models. Using lower-precision training can address these challenges. By reducing the numeric precision used during…
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NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-uniform memory access (NUMA) behaviors, but expose a single memory space. Most programs therefore do not have an issue with memory non-uniformity. However, as bandwidth increases in newer generation GPUs, there are significant performance and power gains to be had when taking into…
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As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges through intelligent scheduling and dynamic GPU fractioning. GPU fractioning is wholly delivered by NVIDIA Run:ai in any environment—cloud, NCP, and on-premises. This post presents the joint benchmarking effort between NVIDIA and AI…
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Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to maintain bindings back to Python. For most Python developers and researchers, this is a significant barrier to entry. Frameworks like PyTorch address this by implementing kernels in CUDA C++—either handwritten or by leveraging libraries…
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As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance.
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Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. Retrieval-augmented generation (RAG) was created to ground…
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Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physical world is expensive, slow, and often dangerous. It is nearly impossible to safely train for real-world critical risks, such as high-speed collisions or hardware failures. Worse, real-world data is usually biased toward “normal” conditions, leaving robots unprepared for the…
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Scientists and engineers who design and build unique scientific research facilities face similar challenges. These include managing massive data rates that exceed current computational infrastructure capacity to extract scientific insights and driving the experiments in real time. These challenges are obstacles to maximizing the impact of scientific discoveries and significantly slow the pace of…
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NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but deploying a new architecture traditionally requires significant manual effort. To address this challenge, today we are announcing the availability of AutoDeploy as a beta feature in TensorRT LLM. AutoDeploy compiles off-the-shelf PyTorch models into inference-optimized…
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The latest AI models continue to grow in size and complexity, demanding increasing amounts of compute performance for training and inference—far beyond what Moore’s Law can keep up with. That’s why NVIDIA engages in extreme codesign. Designing across multiple chips and a mountain of software cohesively enables large generational leaps in AI factory performance and efficiency.
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Specialized AI models are built to perform specific tasks or solve particular problems. But if you’ve ever tried to fine-tune or distill a domain-specific model, you’ve probably hit a few blockers, such as: These challenges often prevent promising AI projects from progressing beyond the experimental phase. This post walks you through how to remove all four of these blockers using a…
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Kimi K2.5 is the newest open vision language model (VLM) from the Kimi family of models. Kimi K2.5 is a general-purpose multimodal model that excels in current high-demand tasks such as agentic AI workflows, chat, reasoning, coding, mathematics, and more. The model was trained using the open source Megatron‑LM framework. Megatron-LM provides accelerated computing for scalability and GPU…
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What if your AI agent could instantly parse complex PDFs, extract nested tables, and “see” data within charts as easily as reading a text file? With NVIDIA Nemotron RAG, you can build a high-throughput intelligent document processing pipeline that handles massive document workloads with precision and accuracy. This post walks you through the core components of a multimodal retrieval pipeline…
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Large language models (LLMs) are rapidly expanding their context windows, with recent models supporting sequences of 128K tokens, 256K tokens, and beyond. However, training these models with extended context lengths presents significant computational and communication challenges. As context lengths grow, the memory and communication overhead of attention mechanisms scale quadratically…
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In LLM training, Expert Parallel (EP) communication for hyperscale mixture-of-experts (MoE) models is challenging. EP communication is essentially all-to-all, but due to its dynamics and sparseness (only topk experts per AI token instead of all experts), it’s challenging to implement and optimize. This post details an efficient MoE EP communication solution, Hybrid-EP, and its use in the…
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NVIDIA CUDA Tile is a GPU-based programming model that targets portability for NVIDIA Tensor Cores, unlocking peak GPU performance. One of the great things about CUDA Tile is that you can build your own DSL on top of it. This post shares the work NVIDIA is doing to integrate CUDA Tile as a backend for OpenAI Triton, an open source Python DSL designed to write DL kernels for GPUs.
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Sparse tensors are vectors, matrices, and higher-dimensional generalizations with many zeros. They are crucial in various fields such as scientific computing, signal processing, and deep learning due to their efficiency in storage, computation, and power. Despite their benefits, handling sparse tensors manually or through existing libraries is often cumbersome, error-prone, nonportable…
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AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a significant, often overlooked, attack surface by running tools from the command line with the same permissions and entitlements as the user, making them computer use agents, with all the risks those entail. The primary threat to these tools is…
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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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This post introduces Dynamic Context Parallelism (Dynamic-CP), a scheduling approach in NVIDIA Megatron Core used for LLM post-training or DiT pre-training. It dynamically selects the CP size per microbatch to efficiently handle variable-length sequences, achieving up to 1.48x speedup on real-world datasets. In large-scale model training, an often-overlooked bottleneck arises from the…
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Advances in AI architectures have unlocked multimodal functionality, enabling transformer models to process multiple forms of data in the same context. For instance, vision language models (VLMs) can generate output from combined image and text input, enabling developers to build systems that interpret graphs, process camera feeds, or operate with traditionally human interfaces like desktop…
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Recent advances in large-scale diffusion models have revolutionized generative AI across multiple domains, from image synthesis to audio generation, 3D asset creation, molecular design, and beyond. These models have demonstrated unprecedented capabilities in producing high-quality, diverse outputs across various conditional generation tasks. Despite these successes…
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Deploying AI applications across diverse consumer hardware has traditionally forced a trade-off. You can optimize for specific GPU configurations and achieve peak performance at the cost of portability. Alternatively, you can build generic, portable engines and leave performance on the table. Bridging this gap often requires manual tuning, multiple build targets, or accepting compromises.
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Global climate models are good at the big picture—but local climate extremes, like hurricanes and typhoons, often disappear in the details. Those patterns are still there—you just need the right tools to unlock them in high-resolution climate data. Using NVIDIA Earth‑2, this blog post shows you how to downscale coarse climate projections into higher-resolution, bias‑corrected fields—revealing…
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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 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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The C++ template library CUB is a go-to for high-performance GPU primitive algorithms, but its traditional “two-phase” API, which separates memory estimation from allocation, can be cumbersome. While this programming model offers flexibility, it often results in repetitive boilerplate code. This post explains the shift from this API to the new CUB single-call API introduced in CUDA 13.1…
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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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This blog post is part of a series designed to help developers learn NVIDIA CUDA Tile programming for building high-performance GPU kernels, using matrix multiplication as a core example. In this post, you’ll learn: Before you begin, be sure your environment meets the following requirements (see the quickstart for more information): Environment requirements: Install…
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NVIDIA DLSS 4 with Multi Frame Generation has become the fastest-adopted NVIDIA gaming technology ever. Over 250 games and apps use it to make real-time path tracing possible—and upcoming titles for 2026, including PRAGMATA and Resident Evil Requiem, also plan to incorporate the software. At CES 2026, the technology became even more powerful. NVIDIA introduced DLSS 4.5…
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NVIDIA cuOpt is a GPU-accelerated optimization engine designed to deliver fast, high-quality solutions for large, complex decision-making problems. Mixed integer programming (MIP) is a technique for solving problems. It can be modeled by a set of linear constraints, with some of the variables able to assume only integer values. The types of problems that can be modeled as MIP are numerous and…
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We keep seeing LLMs with larger context windows in the news, along with promises that they can hold entire conversation histories, volumes of books, or multiple codebases in view at once. And yet, these models still repeat the same mistakes. We still have to copy and paste the earlier context back into the chat for LLMs to “get it”. A smart co-worker would pick up on these patterns, adapt…
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Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep up. Throughput is rising, SLAs are shrinking, and fleets of AMRs, conveyors, and sensors expand every year. But beneath that technological surface, most sites still rely on a familiar trio: a Warehouse Management System (WMS)…
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E-commerce catalogs often contain sparse product data, generic images, a basic title, and short description. This limits discoverability, engagement, and conversion. Manual enrichment doesn’t scale because it relies on catalog managers to manually write descriptions, apply tags, and categorize. The process is slow, inconsistent, and error-prone. This tutorial shows developers…
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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 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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To make humanoid robots useful, they need cognition and loco-manipulation that span perception, planning, and whole-body control in dynamic environments. Building these generalist robots requires a workflow that unifies simulation, control, and learning for robots to acquire complex skills before transferring into the real world. In this post, we present NVIDIA Isaac GR00T N1.6…
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Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want to run conversational AI agents, multimodal perception, and high-level planning directly on the vehicle or robot – where latency, reliability, and the ability to operate offline matter most. While many existing LLM and vision language…
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As robots take on increasingly dynamic mobility tasks, developers need physics-accurate simulations that translate across environments and workloads. Training robot policies and models to do these tasks requires a large amount of diverse, high-quality data, which is often expensive and time-consuming to collect in the physical world. Therefore, generating synthetic data at scale using cloud…
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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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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 developer activity on PCs is exploding, driven by the rising quality of small language models (SLMs) and diffusion models, such as FLUX.2, GPT-OSS-20B, and Nemotron 3 Nano. At the same time, AI PC frameworks, including ComfyUI, llama.cpp, Ollama, and Unsloth are making functional advances, doubling in popularity over the past year as the number of developers using PC-class models has grown…
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Since its release, NVIDIA has continued to push performance of the Grace Blackwell-powered DGX Spark through continuous software optimization and close collaboration with software partners and the open-source community. These efforts are delivering meaningful gains across inference, training and creative workflows. At CES 2026, the latest DGX Spark software release, combined with new model…
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Update March 16, 2026: The NVIDIA Vera Rubin platform now has a seventh chip. Learn more about NVIDIA Groq 3 LPX: The Low-Latency Inference Accelerator for the NVIDIA Vera Rubin Platform. 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…
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Generalist robot policies must operate across diverse tasks, embodiments, and environments, requiring scalable, repeatable simulation-based evaluation. Setting up large-scale policy evaluations is tedious and manual. Without a systematic approach, developers need to build high-overhead custom infrastructure, yet task libraries remain limited in complexity and diversity.
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NVIDIA is introducing the NVIDIA Jetson T4000, bringing high-performance AI and real-time reasoning to a wider range of robotics and edge AI applications. Optimized for tighter power and thermal envelopes, T4000 delivers up to 1200 FP4 TFLOPs of AI compute and 64 GB of memory, providing an ideal balance of performance, efficiency, and scalability. With its energy-efficient design and production…
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Building an agent is more than just “call an API”—it requires stitching together retrieval, speech, safety, and reasoning components so they behave like one cohesive system. Each layer has its own interface, latency constraints, and integration challenges, and you start to feel them as soon as you move beyond a simple prototype. In this tutorial, you’ll learn how to build a voice-powered RAG…
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Autonomous vehicle (AV) research is undergoing a rapid shift. The field is being reshaped by the emergence of reasoning-based vision–language–action (VLA) models that bring human-like thinking to AV decision-making. These models can be viewed as implicit world models operating in a semantic space, allowing AVs to solve complex problems step-by-step and to generate reasoning traces that mirror…
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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 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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Machine learning interatomic potentials (MLIPs) are transforming the landscape of computational chemistry and materials science. MLIPs enable atomistic simulations that combine the fidelity of computationally expensive quantum chemistry with the scaling power of AI. Yet, developers working at this intersection face a persistent challenge: a lack of robust, Pythonic toolbox for GPU…
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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 (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 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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Solving large-scale problems in Electronic Design Automation (EDA), Computational Fluid Dynamics (CFD), and advanced optimization workflows has become the norm as chip designs, manufacturing, and multi-physics simulations have grown in complexity. These workloads push traditional solvers and require unprecedented scalability and performance. The NVIDIA CUDA Direct Sparse Solver (cuDSS) is built…
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