NVIDIA Dev Blog

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and... The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern… Source

Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don't... Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so… Source

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making.... Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making. Traditionally, specialized operations research (OR) teams solved these problems by translating business questions into mathematical models. This process can take weeks and often produces fragile solutions that struggle to adapt when conditions… Source

Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation 6X, and the second-generation... Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation 6X, and the second-generation transformer model for NVIDIA Super Resolution. In this post, we’ll go over new technologies and resources to share with our game-developer community, including: At CES 2026, we introduced DLSS 4.5, extending its AI-driven… Source

Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches... Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches like super resolution, denoising, and neural rendering help real-time engines work more efficiently, offering new creative possibilities while keeping performance in mind. Unreal Engine 5 (UE5) has taken several steps in this direction… Source

Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks... Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks that once took hours of manual effort into automated, repeatable pipelines. ComfyUI is an open-source, node-based creative tool that runs locally on NVIDIA RTX GPUs. It connects image generation, video synthesis, and language models into… Source

NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores, and... NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores, and matrix multiply-accumulate—rather than manually coordinating threads, warps, and shared memory. cuTile.jl brings the same tile-based approach to the dynamic programming language Julia. Users can write custom GPU kernels without dropping… Source

The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and... The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and real-time decision-making at scale, competitive advantage increasingly depends on the infrastructure that supports them. Success requires more than raw compute. It demands a scalable, predictable foundation that can orchestrate intelligent… Source

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,... For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragments—single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Now… Source

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on... Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented model chains—separate stacks for vision, audio, and text. This increases inference hops and orchestration complexity, driving up inference costs while weakening cross-modal context consistency. NVIDIA Nemotron 3 Nano Omni… Source

The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential... The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential and time-intensive manual workflows. As data complexity grows, the gap between machine speed and human bandwidth has become a primary bottleneck. On-demand simulation workflows are currently hampered by both manual data overhead… Source

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient... DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million-token context inference. DeepSeek-V4-Pro is the largest model in the family, with 1.6T total parameters and 49B active parameters. DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, designed for higher-speed… Source

Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable.... Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale. Source

In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground... In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground competition. Success in modern machine learning competitions is increasingly defined by how quickly you can generate, test, and iterate on ideas. LLM agents, combined with GPU acceleration, dramatically compress this loop. Historically… Source

In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater... In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater flexibility and performance. We’re excited to announce the integration of the UST into nvmath-python v0.9.0 to accelerate sparse scientific and deep learning applications. This post provides a walkthrough of key UST features… Source

AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools.... AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools. This shift requires the modern data center to move beyond single-purpose silos. For developers, gaining access to dedicated GPU compute can often be a bottleneck. Virtual machines (VMs) solve part of this challenge by providing secure… Source

Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved... Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved significant success more recently when applied to leading LLMs. In particular, Muon (MomentUm Orthogonalized by Newton-Schulz) was used to train some of today’s best open source models, including Kimi K2 and GLM-5. Source

The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these... The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these models at the edge, enabling physical AI agents and autonomous robots to automate heavy-duty tasks. A key challenge is efficiently running multi-billion-parameter models on edge devices with limited memory. With ongoing constraints on… Source

As LLMs transition from simple text generation to complex reasoning, reinforcement learning (RL) plays a central role. Algorithms like Group Relative Policy... As LLMs transition from simple text generation to complex reasoning, reinforcement learning (RL) plays a central role. Algorithms like Group Relative Policy Optimization (GRPO) power this transition, enabling reasoning-grade models to continuously improve through iterative feedback. Unlike standard supervised fine-tuning, RL training loops are bifurcated into two distinct, high-intensity phases: a… Source

AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating... AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating repetitive tasks, executing tasks, writing documentation, and more. OpenAI Codex, for example, is a coding agent designed to assist developers through tasks like code generation, debugging, and automated pull request (PR) creation. Source

Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents.... Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents. Spotify reports 650+ agent-generated PRs per month. Tools like Claude Code and Codex make hundreds of API calls per coding session, each carrying the full conversation history. Behind every one of these workflows is an inference stack under… Source

Agents are evolving from question-and-answer systems into long-running autonomous assistants that read files, call APIs, and drive multi-step workflows.... Source

The development of socially acceptable nuclear reactors requires that they are safe, clean, efficient, economical, and sustainable. Meeting these requirements... The development of socially acceptable nuclear reactors requires that they are safe, clean, efficient, economical, and sustainable. Meeting these requirements calls for new approaches, driving growing interest in Small Modular Reactors (SMRs) and in Generation IV designs. SMRs aim to improve project economics by standardising designs and shifting construction to controlled manufacturing… Source

Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code,... Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code, and lengthy development cycles. NVIDIA DeepStream 9 removes these development barriers using coding agents, such as Claude Code or Cursor, to help you easily create deployable, optimized code that brings your vision AI applications to… Source

For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density functional theory (DFT) provide high... For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density functional theory (DFT) provide high fidelity but are computationally expensive, limiting researchers to systems of a few hundred atoms. Conversely, classical force fields are fast but often lack the chemical accuracy required for complex bond-breaking or transition-state analysis. Source

When you’re writing CUDA applications, one of the most important things you need to focus on to write great code is data transfer performance. This applies to... When you’re writing CUDA applications, one of the most important things you need to focus on to write great code is data transfer performance. This applies to both single-GPU and multi-GPU systems alike. One of the tools you can use to understand the memory characteristics of your GPU system is NVIDIA NVbandwidth. In this blog post, we’ll explore what NVbandwidth is, how it works… Source

NVIDIA Ising is the world's first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising... NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising Decoding. Both target the fundamental challenge in quantum computing—qubits are inherently noisy. The best quantum processors make an error roughly once in every thousand operations. To become useful accelerators for scientific and… Source

The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses,... The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as reasoning, ML research workflows, software, engineering, and office work. The open weights release of MiniMax M2.7 is now available through NVIDIA and across the open source inference ecosystem. The MiniMax M2 series is a sparse mixture-of… Source

Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems. Most organizations... Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems. Most organizations running large-scale AI training have years of investment in Slurm job scripts, fair-share policies, and accounting workflows. The challenge is getting Slurm scheduling capabilities onto Kubernetes—the standard platform for managing GPU… Source

Training LLMs requires periodic checkpoints. These full snapshots of model weights, optimizer states, and gradients are saved to storage so training can resume... Training LLMs requires periodic checkpoints. These full snapshots of model weights, optimizer states, and gradients are saved to storage so training can resume after interruptions. At scale, these checkpoints become massive (782 GB for a 70B model) and frequent (every 15-30 minutes), generating one of the largest line items in a training budget. Most AI teams chase GPU utilization… Source

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming... Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming protein complexes whose structures are described in the hierarchy of protein structure as the quaternary representation. This represents one level of complexity up from tertiary representations, the 3D structure of monomers… Source

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and... Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and industrial systems, long before anything ships to the factory floor. At GTC 2026, NVIDIA highlighted physical AI as a key direction for robotics and digital twins, where policies are trained and validated against physically grounded environments. Source

The NVIDIA GB200 NVL72 and NVIDIA GB300 NVL72 systems, featuring NVIDIA Blackwell architecture, are rack-scale supercomputers. They’re designed with 18... The NVIDIA GB200 NVL72 and NVIDIA GB300 NVL72 systems, featuring NVIDIA Blackwell architecture, are rack-scale supercomputers. They’re designed with 18 tightly coupled compute trays, massive GPU fabrics, and high-bandwidth networking packaged as a unit. For AI architects and HPC platform operators, the challenge isn’t just racking and stacking hardware—it’s turning infrastructure into safe… Source

In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including decode, preprocessing, and GPU... 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… Source

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... 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… Source

In algorithmic trading, reducing response times to market events is crucial. To keep pace with high-speed electronic markets, latency-sensitive firms often use... 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… Source

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... 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… Source

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... 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… Source

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... 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… Source

Spatial computing is moving from visualization to active collaboration, adding increasingly more GPU demands on XR hardware to render photorealistic,... 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… Source

Delivering high-fidelity VR and AR experiences to enterprise users has typically required native application development, custom device management, and complex... 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. Source

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition... 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… Source

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... 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… Source

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or... 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… Source

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... 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… Source

Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale,... 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… Source

Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime... 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. Source

AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud. This includes sensitive information like... 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. Source

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages... 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… Source

While consumer AI offers powerful capabilities, workplace tools often suffer from disjointed data and limited context. Built with LangChain, the NVIDIA AI-Q... 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… Source