#gpu

Synthesize Realistic 3D Medical Images at Scale to Ship Pre‑Trained Models

High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by …

Building Token‑Metered AI Services on Telco AI Factories

Telcos around the world are building sovereign AI factories based on the NVIDIA Cloud Partner (NCP) reference architectu…

Unlock Exascale Performance on NVIDIA GB200 NVL72 with Slurm Topology-Aware Job Scheduling

As AI models grow in scale and complexity, realizing the full performance of modern accelerated infrastructure depends a…

NVIDIA-Verified Agent Skills Provide Capability Governance for AI Agents

Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable …

Google's SynthID AI watermarking tech is being adopted by OpenAI, Nvidia, and more

In a few short years, we’ve gone from easily identifying AI content that featured superfluous fingers to images and vide…

vLLM and PyTorch Work Together to Improve the Developer Experience on aarch64

Featured projects TLDR: PyTorch 2.11 makes it possible to install CUDA-enabled PyTorch wheels on aarch64 Linux directly …

How the NVIDIA Vera Rubin Platform is Solving Agentic AI’s Scale-Up Problem

Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic…

PyTorch 2.12 Release Blog

Featured projects We are excited to announce the release of PyTorch® 2.12 (release notes)! The PyTorch 2.12 release feat…

Transform Video Into Instantly Searchable, Actionable Intelligence with AI Agents and Skills

In today’s data-driven world, organizations increasingly rely on video to capture critical information, yet extracting m…

How NVIDIA engineers and researchers build with Codex

How NVIDIA engineers and researchers build with Codex Teams use Codex with GPT‑5.5 to ship production systems and turn r…

Introducing NVIDIA Fleet Intelligence for Real-Time GPU Fleet Visibility and Optimization

The compute capability of large GPU fleets presents unprecedented opportunities to innovate and provide value to custome…

Building Blocks for Foundation Model Training and Inference on AWS

Building Blocks for Foundation Model Training and Inference on AWS Figure: Adapted from "AI's Three Scaling Laws, Explai…

CUDA Proves Nvidia Is a Software Company

Forgive me for starting with a cliché, a piece of finance jargon that has recently slipped into the tech lexicon, but I’…

Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding

Bash is one of the most flexible and powerful interfaces exposed to AI agents. In the right system, a model that emits g…

Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in NVIDIA Dynamo

An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool c…

MedQA: Fine-Tuning a Clinical AI on AMD ROCm — No CUDA Required

MedQA: Fine-Tuning a Clinical AI on AMD ROCm — No CUDA Required The Idea Medical question answering is one of those task…

Real-Time Performance Monitoring and Faster Debugging with NCCL Inspector and Prometheus

Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication L…

Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer

Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices suc…

Achieving Peak System and Workload Efficiency on NVIDIA GB200 NVL72 with Slurm Block Scheduling

NVIDIA GB200 NVL72 introduces a fundamentally new way to build GPU clusters by extending NVIDIA NVLink coherence across …

How to Build In-Vehicle AI Agents with NVIDIA: From Cloud to Car

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems ca…

Optimize Supply Chain Decision Systems Using NVIDIA cuOpt Agent Skills

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, a…

Pentagon strikes classified AI deals with OpenAI, Google, and Nvidia — but not Anthropic

The Pentagon has struck deals with OpenAI, Google, Microsoft, Amazon, Nvidia, Elon Musk’s xAI, and the startup Reflectio…

Automating GPU Kernel Translation with AI Agents: cuTile Python to cuTile.jl

NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of til…

Build AI-Powered Games with NVIDIA DLSS 4.5, RTX, and Unreal Engine 5

Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation…

Speed Up Unreal Engine NNE Inference with NVIDIA TensorRT for RTX Runtime

Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and st…

All the evidence unveiled so far in Musk v. Altman

The Musk v. Altman trial is underway, and that means exhibits, or the evidence to be presented in court, are being revea…

Powering AI Factories with NVIDIA Enterprise Reference Architectures

The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capa…

Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM Apr 28, 2026 · 7 min read We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM.

Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM We are excited to support the new…

Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM Apr 28, 2026 · 7 min read We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM.

Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM We are excited to support the new…

NVIDIA Nemotron 3 Nano Omni Powers Multimodal Agent Reasoning in a Single Efficient Open Model

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop…

Scaling Biomolecular Modeling Using Context Parallelism in NVIDIA BioNeMo

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into …

Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents

Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents - NV…

NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart

Artificial Intelligence NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart Today, we are exci…

Federated Learning Without the Refactoring Overhead Using NVIDIA FLARE

Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valua…

Build with DeepSeek V4 Using NVIDIA Blackwell and GPU-Accelerated Endpoints

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targete…

Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron

Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at lea…

Scaling the AI-Ready Data Center with NVIDIA RTX PRO 4500 Blackwell Server Edition and NVIDIA vGPU 20

AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to mor…

Disaggregated Serving for Hybrid SSM Models in vLLM Apr 21, 2026 · 15 min read Hybrid architectures that interleave Mamba-style SSM layers with standard full-attention (FA) layers — such as NVIDIA Nemotron-H — are gaining traction as a way to combine the linear-time...

Disaggregated Serving for Hybrid SSM Models in vLLM Introduction Hybrid architectures that interleave Mamba-style SSM la…

Disaggregated Serving for Hybrid SSM Models in vLLM Apr 21, 2026 · 15 min read Hybrid architectures that interleave Mamba-style SSM layers with standard full-attention (FA) layers — such as NVIDIA Nemotron-H — are gaining traction as a way to combine the linear-time...

Disaggregated Serving for Hybrid SSM Models in vLLM Introduction Hybrid architectures that interleave Mamba-style SSM la…

Maximizing Memory Efficiency to Run Bigger Models on NVIDIA Jetson

The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical worl…

Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances

Artificial Intelligence Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances As the demand for g…

Build a More Secure, Always-On Local AI Agent with OpenClaw and NVIDIA NemoClaw

Agents are evolving from question-and-answer systems into long-running autonomous assistants that read files, call APIs,…

Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo

Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attribu…

How to Build Vision AI Pipelines Using NVIDIA DeepStream Coding Agents

Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate d…

NVIDIA Ising Introduces AI-Powered Workflows to Build Fault-Tolerant Quantum Systems

NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model dom…

NVIDIA NVbandwidth: Your Essential Tool for Measuring GPU Interconnect and Memory Performance

When you’re writing CUDA applications, one of the most important things you need to focus on to write great code is data…

Building Custom Atomistic Simulation Workflows for Chemistry and Materials Science with NVIDIA ALCHEMI Toolkit

For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density f…

MiniMax M2.7 Advances Scalable Agentic Workflows on NVIDIA Platforms for Complex AI Applications

The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other …

Cut Checkpoint Costs with About 30 Lines of Python and NVIDIA nvCOMP

Training LLMs requires periodic checkpoints. These full snapshots of model weights, optimizer states, and gradients are …

SOTA Normalization Performance with torch.compile

Introduction Normalization methods (LayerNorm/RMSNorm) are foundational in deep learning and are used to normalize value…

Faster Diffusion on Blackwell: MXFP8 and NVFP4 with Diffusers and TorchAO

Diffusion models for image and video generation have been surging in popularity, delivering super-realistic visual media…

Integrate Physical AI Capabilities into Existing Apps with NVIDIA Omniverse Libraries

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how team…

Running AI Workloads on Rack-Scale Supercomputers: From Hardware to Topology-Aware Scheduling

The NVIDIA GB200 NVL72 and NVIDIA GB300 NVL72 systems, featuring NVIDIA Blackwell architecture, are rack-scale supercomp…

Accelerating Vision AI Pipelines with Batch Mode VC-6 and NVIDIA Nsight

In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including d…

NVIDIA Platform Delivers Lowest Token Cost Enabled by Extreme Co-Design

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost…

CUDA Tile Programming Now Available for BASIC!

Note: CUDA Tile Programming in BASIC is an April Fools’ joke, but it’s also real and actually works, demonstrating the f…

Build and Stream Browser-Based XR Experiences with NVIDIA CloudXR.js

Delivering high-fidelity VR and AR experiences to enterprise users has typically required native application development…

Stream High-Fidelity Spatial Computing Content to Any Device with NVIDIA CloudXR 6.0

Spatial computing is moving from visualization to active collaboration, adding increasingly more GPU demands on XR hardw…

Enabling Up to 41% Faster Pre-training: MXFP8 and DeepEP for DeepSeek-V3 on B200 with TorchTitan

TL;DR In a joint effort between PyTorch and Nebius, we enabled training DeepSeek-V3 Mixture-of-Experts models (16B and 6…

Designing Protein Binders Using the Generative Model Proteina-Complexa

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or prot…

How Centralized Radar Processing on NVIDIA DRIVE Enables Safer, Smarter Level 4 Autonomy

In the current state of automotive radar, machine learning engineers can’t work with camera-equivalent raw RGB images. I…

Building NVIDIA Nemotron 3 Agents for Reasoning, Multimodal RAG, Voice, and Safety

Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety g…

NVIDIA IGX Thor Powers Industrial, Medical, and Robotics Edge AI Applications

Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, hum…

Build a Domain-Specific Embedding Model in Under a Day

Build a Domain-Specific Embedding Model in Under a Day With a single GPU and less than a day of training time, you can t…

How to Build Deep Agents for Enterprise Search with NVIDIA AI-Q and LangChain

While consumer AI offers powerful capabilities, workplace tools often suffer from disjointed data and limited context. B…

Building the AI Grid with NVIDIA: Orchestrating Intelligence Everywhere

AI-native services are exposing a new bottleneck in AI infrastructure: As millions of users, agents, and devices demand …

Newton Adds Contact-Rich Manipulation and Locomotion Capabilities for Industrial Robotics

Physics forms the foundation of robotic simulation, enabling realistic modeling of motion and interaction. For tasks lik…

NVIDIA Vera Rubin POD: Seven Chips, Five Rack-Scale Systems, One AI Supercomputer

Artificial intelligence is token-driven. Every prompt, reasoning step, and agent interaction generates tokens. Over the …

Inside NVIDIA Groq 3 LPX: The Low-Latency Inference Accelerator for the NVIDIA Vera Rubin Platform

NVIDIA Groq 3 LPX is a new rack-scale inference accelerator for the NVIDIA Vera Rubin platform, designed for the low-lat…

Run Autonomous, Self-Evolving Agents More Safely with NVIDIA OpenShell

AI has evolved from assistants following your directions to agents that act independently. Called claws, these agents ca…

NVIDIA Vera CPU Delivers High Performance, Bandwidth, and Efficiency for AI Factories

AI is evolving, and reasoning models are increasing token demand, placing new requirements on every layer of AI infrastr…

Design, Simulate, and Scale AI Factory Infrastructure with NVIDIA DSX Air

Building AI factories is complex and requires efficient integration across compute, networking, security, and storage sy…

Introducing NVIDIA BlueField-4-Powered CMX Context Memory Storage Platform for the Next Frontier of AI

AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions o…

Scaling Autonomous AI Agents and Workloads with NVIDIA DGX Spark

Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that …

How NVIDIA Dynamo 1.0 Powers Multi-Node Inference at Production Scale

Reasoning models are growing rapidly in size and are increasingly being integrated into agentic AI workflows that intera…

Scale Synthetic Data and Physical AI Reasoning with NVIDIA Cosmos World Foundation Models

The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware t…

Build Accelerated, Differentiable Computational Physics Code for AI with NVIDIA Warp

Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics founda…

Run Highly Efficient and Accurate Multi-Agent AI with NVIDIA Nemotron 3 Super Using vLLM Mar 11, 2026 · 5 min read We are excited to support the newly released NVIDIA Nemotron 3 Super model on vLLM.

Run Highly Efficient and Accurate Multi-Agent AI with NVIDIA Nemotron 3 Super Using vLLM We are excited to support the n…

Run Highly Efficient and Accurate Multi-Agent AI with NVIDIA Nemotron 3 Super Using vLLM Mar 11, 2026 · 5 min read We are excited to support the newly released NVIDIA Nemotron 3 Super model on vLLM.

Run Highly Efficient and Accurate Multi-Agent AI with NVIDIA Nemotron 3 Super Using vLLM We are excited to support the n…

NVIDIA RTX Innovations Are Powering the Next Era of Game Development

NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new st…

Enhancing Distributed Inference Performance with the NVIDIA Inference Transfer Library

Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and r…

Implementing Falcon-H1 Hybrid Architecture in NVIDIA Megatron Core

In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the fou…

CUDA 13.2 Introduces Enhanced CUDA Tile Support and New Python Features

CUDA 13.2 arrives with a major update: NVIDIA CUDA Tile is now supported on devices of compute capability 8.X architectu…

Import AI 448: AI R&D; Bytedance's CUDA-writing agent; on-device satellite AI

Import AI 448: AI R&D; Bytedance's CUDA-writing agent; on-device satellite AI If Ukraine is the first major drone war, w…

Controlling Floating-Point Determinism in NVIDIA CCCL

A computation is considered deterministic if multiple runs with the same input data produce the same bitwise result. Whi…

Tuning Flash Attention for Peak Performance in NVIDIA CUDA Tile

In this post, we dive into one of the most critical workloads in modern AI: Flash Attention, where you’ll learn: - How t…

cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia

NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to ten…

How to Minimize Game Runtime Inference Costs with Coding Agents

NVIDIA ACE is a suite of technologies for building AI agents for gaming. ACE provides ready-to-integrate cloud and on-de…

Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

Autonomous networks are quickly becoming one of the top priorities in telecommunications. According to the latest NVIDIA…

Scaling AI for everyone

Scaling AI for everyone AI demand is surging across consumers, developers, and businesses. Meeting that demand and provi…

Maximizing GPU Utilization with NVIDIA Run:ai and NVIDIA NIM

Organizations deploying LLMs are challenged by inference workloads with different resource requirements. A small embeddi…

Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints

Alibaba has introduced the new open source Qwen3.5 series built for native multimodal agents. The first model in this se…

Making Softmax More Efficient with NVIDIA Blackwell Ultra

LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent …

Cerebras CS-3 vs. Nvidia DGX B200 Blackwell September 19, 2025

Cerebras delivers the world’s fastest AI infrastructure TL;DR The Cerebras CS-3 system is 21x faster, 1/3 lower cost, an…

Accelerating Data Processing with NVIDIA Multi-Instance GPU and Locality Domains

NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-unif…

How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models

As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performanc…

Topping the GPU MODE Kernel Leaderboard with NVIDIA cuda.compute

Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping in…

Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai

As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. N…

DeepSeek-V3.2 on GB300: Performance Breakthrough Feb 13, 2026 · 12 min read DeepSeek-V3.2 (NVFP4 + TP2)has been successfully and smoothly run on GB300 (SM103 - Blackwell Ultra). Leveraging FP4 quantization, it achieves a single-GPU throughput of 7360 TGS (tokens / GPU /...

DeepSeek-V3.2 on GB300: Performance Breakthrough Summary DeepSeek-V3.2 (NVFP4 + TP2)has been successfully and smoothly r…

R²D²: Scaling Multimodal Robot Learning with NVIDIA Isaac Lab

Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physic…