NVIDIA Developer Blog

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NVIDIA Developer Blog official-blog 2mo ago

Running Large-Scale GPU Workloads on Kubernetes with Slurm

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...

33
NVIDIA Developer Blog official-blog 2mo ago

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 saved to storage so training can resume...

38
NVIDIA Developer Blog official-blog 2mo ago

How to Accelerate Protein Structure Prediction at Proteome-Scale

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming...

29
NVIDIA Developer Blog official-blog 2mo ago

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 teams design and validate robots and...

9
NVIDIA Developer Blog official-blog 2mo ago

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 supercomputers. They’re designed with 18...

32
NVIDIA Developer Blog official-blog 2mo ago

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 decode, preprocessing, and GPU...

17
NVIDIA Developer Blog official-blog 2mo ago

Bringing AI Closer to the Edge and On-Device with Gemma 4

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...

27
NVIDIA Developer Blog official-blog 2mo ago

Achieving Single-Digit Microsecond Latency Inference for Capital Markets

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

18
NVIDIA Developer Blog official-blog 2mo ago

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 flexibility of CUDA. CUDA 13.1...

5
NVIDIA Developer Blog official-blog 2mo ago

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. Measuring this goes far beyond peak...

14
NVIDIA Developer Blog official-blog 2mo ago

Accelerate Token Production in AI Factories Using Unified Services and Real-Time AI

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...

10
NVIDIA Developer Blog official-blog 3mo ago

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 hardware to render photorealistic,...

28
NVIDIA Developer Blog official-blog 3mo ago

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, custom device management, and complex...

6
NVIDIA Developer Blog official-blog 3mo ago

Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads

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

38
NVIDIA Developer Blog official-blog 3mo ago

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. Instead, they work with the output of...

22
NVIDIA Developer Blog official-blog 3mo ago

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 proteins that bind to a target protein or...

18
NVIDIA Developer Blog official-blog 3mo ago

Scaling Token Factory Revenue and AI Efficiency by Maximizing Performance per Watt

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...

4
NVIDIA Developer Blog official-blog 3mo ago

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 guardrailing. As these systems scale,...

37
NVIDIA Developer Blog official-blog 3mo ago

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, human-machine interaction, and downtime...

13
NVIDIA Developer Blog official-blog 3mo ago

Building a Zero-Trust Architecture for Confidential AI Factories

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

30
NVIDIA Developer Blog official-blog 3mo ago

Deploying Disaggregated LLM Inference Workloads on Kubernetes

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

15
NVIDIA Developer Blog official-blog 3mo ago

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. Built with LangChain, the NVIDIA AI-Q...

30
NVIDIA Developer Blog official-blog 3mo ago

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 access to intelligence, the challenge is...

14
NVIDIA Developer Blog official-blog 3mo ago

Using Simulation to Build Robotic Systems for Hospital Automation

Healthcare faces a structural demand–capacity crisis: a projected global shortfall of ~10 million clinicians by 2030, billions of diagnostic exams annually...

17
NVIDIA Developer Blog official-blog 3mo ago

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 of tokens and models scale toward...

27
NVIDIA Developer Blog official-blog 3mo ago

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 interact with other models and external tools....

6
NVIDIA Developer Blog official-blog 3mo ago

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 use multiple communication channels and...

23
NVIDIA Developer Blog official-blog 3mo ago

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 systems. To achieve rapid Time to AI and...

10
NVIDIA Developer Blog official-blog 3mo ago

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 infrastructure. More than ever, compute must...

11
NVIDIA Developer Blog official-blog 3mo ago

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 can take a goal, figure out how to achieve...

17
NVIDIA Developer Blog official-blog 3mo ago

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-latency and large-context demands of...

20
NVIDIA Developer Blog official-blog 3mo ago

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 past year, token consumption has grown...

33
NVIDIA Developer Blog official-blog 3mo ago

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 like locomotion and manipulation,...

30
NVIDIA Developer Blog official-blog 3mo ago

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 training data. Without diverse and...

34
NVIDIA Developer Blog official-blog 3mo ago

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 foundation models that generalize across...

38
NVIDIA Developer Blog official-blog 3mo ago

Validate Kubernetes for GPU Infrastructure with Layered, Reproducible Recipes

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...

32
NVIDIA Developer Blog official-blog 3mo ago

Build Next-Gen Physical AI with Edge‑First LLMs for Autonomous Vehicles and Robotics

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...

34
NVIDIA Developer Blog official-blog 3mo ago

Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and long-context...

6
NVIDIA Developer Blog official-blog 3mo ago

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 standard for visuals and performance. At...

8
NVIDIA Developer Blog official-blog 3mo ago

Reliable AI Coding for Unreal Engine: Improving Accuracy and Reducing Token Costs

Agentic code assistants are moving into daily game development as studios build larger worlds, ship more DLCs, and support distributed teams. These assistants...

31
NVIDIA Developer Blog official-blog 3mo ago

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 architectures (NVIDIA Ampere and NVIDIA Ada), as...

18
NVIDIA Developer Blog official-blog 3mo ago

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 foundational framework for training massive...

14
NVIDIA Developer Blog official-blog 3mo ago

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 request handling across many GPUs and...

32
NVIDIA Developer Blog official-blog 3mo ago

Removing the Guesswork from Disaggregated Serving

Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal...

37
NVIDIA Developer Blog official-blog 3mo ago

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 to implement Flash Attention using NVIDIA...

37
NVIDIA Developer Blog official-blog 3mo ago

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. While this may seem like a simple property...

32
NVIDIA Developer Blog official-blog 3mo ago

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-device AI models for every part of in-game...

23
NVIDIA Developer Blog official-blog 3mo ago

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 tensor cores and other specialized...

29
NVIDIA Developer Blog official-blog 4mo ago

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 State of AI in Telecommunications...

25
NVIDIA Developer Blog official-blog 4mo ago

5 New Digital Twin Products Developers Can Use to Build 6G Networks

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...

7

Running Large-Scale GPU Workloads on Kubernetes with Slurm

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

How to Accelerate Protein Structure Prediction at Proteome-Scale

Integrate Physical AI Capabilities into Existing Apps with NVIDIA Omniverse Libraries

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

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

Bringing AI Closer to the Edge and On-Device with Gemma 4

Achieving Single-Digit Microsecond Latency Inference for Capital Markets

CUDA Tile Programming Now Available for BASIC!

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

Accelerate Token Production in AI Factories Using Unified Services and Real-Time AI

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

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

Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads

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

Designing Protein Binders Using the Generative Model Proteina-Complexa

Scaling Token Factory Revenue and AI Efficiency by Maximizing Performance per Watt

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

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

Building a Zero-Trust Architecture for Confidential AI Factories

Deploying Disaggregated LLM Inference Workloads on Kubernetes

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

Building the AI Grid with NVIDIA: Orchestrating Intelligence Everywhere

Using Simulation to Build Robotic Systems for Hospital Automation

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

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

Scaling Autonomous AI Agents and Workloads with NVIDIA DGX Spark

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

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

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

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

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

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

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

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

Validate Kubernetes for GPU Infrastructure with Layered, Reproducible Recipes

Build Next-Gen Physical AI with Edge‑First LLMs for Autonomous Vehicles and Robotics

Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning

NVIDIA RTX Innovations Are Powering the Next Era of Game Development

Reliable AI Coding for Unreal Engine: Improving Accuracy and Reducing Token Costs

CUDA 13.2 Introduces Enhanced CUDA Tile Support and New Python Features

Implementing Falcon-H1 Hybrid Architecture in NVIDIA Megatron Core

Enhancing Distributed Inference Performance with the NVIDIA Inference Transfer Library

Removing the Guesswork from Disaggregated Serving

Tuning Flash Attention for Peak Performance in NVIDIA CUDA Tile

Controlling Floating-Point Determinism in NVIDIA CCCL

How to Minimize Game Runtime Inference Costs with Coding Agents

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

Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

5 New Digital Twin Products Developers Can Use to Build 6G Networks