Blogsby GlyphNov 6
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For...
NVIDIA Technical Blog the engineering blog of Nvidia: latest posts and feed
Nvidia · Engineering blog · 10 a week · 164 posts since Jul 13, 2026
Nov 6
Speed Up Image Generation with FLUX 3 Image Klein on NVIDIA Blackwell GPUs
When deploying diffusion models in design tools, image editors, and visual production workflows, developers must account for inference latency. Generating an...
Nov 5
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent on a redundant weight copy, an...
Nov 5
Configure, Certify, and Operate Kubernetes GPU Clusters with NVIDIA DSX OS
One underperforming GPU can slow an entire distributed training job. The node may pass basic health checks and show normal utilization, leaving operators to...
Nov 4
Use NVIDIA NeMo to Turn Agent Failures into Tests and Harness Fixes
Turn production evidence into prioritized issues and custom evaluations that help you improve prompts, tools, models, memory, skills, and harness code. When you...
Nov 4
How NVIDIA Factory Operations Blueprint Coordinates AI Agents to Investigate Production Issues
A Siemens report estimates that unplanned downtime costs an average large plant in the surveyed sectors $253 million per year. Whether investigating a failed...
Nov 4
NVIDIA CUDA 13.5 Brings NVIDIA Vera Rubin GA Support
As artificial intelligence and accelerated computing continue to push the boundaries of what's possible, the latest NVIDIA CUDA release arrives to unlock...
Nov 4
How NVIDIA Uses AI Agents to Streamline AI Factory Operations
As GPU clusters scale, operations span compute, network, hardware, and scheduling. Site reliability engineers (SREs) often have to examine multiple dashboards...
Nov 2
Support New AI Frameworks Faster Using NVIDIA Workload Map
NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source,...
Nov 2
Build World Models for Interactive Simulation with NVIDIA Cosmos
Physical AI developers working with world models face two distinct challenges: adapting models to their sensors, tasks, and data, and getting those models into...
Nov 2
Making Kubernetes AI-Ready for NVIDIA Vera Rubin on Day One
Configuring and optimizing Kubernetes for new GPU hardware such as NVIDIA Vera Rubin is complex and error-prone. Drivers, kernels, runtimes, networking,...
Nov 2
Keep Distributed Training Running with Automated Workload Recovery
As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery...
Nov 2
Diagnose Distributed AI Workload Failures with NVIDIA Attribution
In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as NCCL errors on other ranks, scheduler state...
Oct 30
How to Check and Configure NVIDIA GPUDirect Storage for Your Platform
NVIDIA GPUDirect Storage (GDS) accelerates GPU storage access in on-premises and cloud environments. It provides a robust, portable, and efficient data path...
Oct 30
Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw
Building an autonomous agent requires a model, a harness, and domain-specific tools. To deploy an agent more securely, developers need a runtime that enforces...
Oct 30
Run AI Models for Edge Application Development with Just a Few Commands
AI is expanding beyond the data center as developers build a new generation of agents to act at the edge: monitoring driver attention and cabin conditions,...
Oct 30
Build a Validated Kubernetes Runtime for NVIDIA Dynamo
Teams building AI applications need a repeatable path from a model configuration to a working inference endpoint. NVIDIA Dynamo is a distributed inference...
Oct 29
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...
Oct 27
3 Ways to Boost MoE Inference on NVIDIA DGX Spark
Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA...
Oct 27
Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages
As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve...
Oct 27
NVIDIA RTX Spark Powers Neural Rendering and AI Characters in Games
NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX...
Oct 27
Memory-Enforced GPU Sharing in Kubernetes with NVIDIA CUDA 13.4
Many AI workloads need only a fraction of a GPU. Giving each one an entire device can leave capacity idle; sharing GPUs lets platform teams serve more workloads...
Oct 26
How NVIDIA Mission Control 2.5 Brings Autonomous Recovery to Kubernetes
Training large-scale AI models on thousands of GPUs is a high-stakes operation where monitoring and automation must be built-in to optimize cluster resiliency...
Oct 26
How NVIDIA Minerva Turns Telemetry Chaos Into Peak GPU Yield
AI Factories are some of the most technologically complex operational environments ever created. Hundreds of thousands of GPUs, NVIDIA Spectrum-X Ethernet...
Oct 23
Using AI Agents to Teach Robots Like Microduck to Walk in Simulation
Teaching a robot to navigate an obstacle course requires more than a walking policy. It must perceive its surroundings, maintain balance, and adjust its actions...
About this blog
CUDA, inference, training at scale and accelerated computing.
Posts per week, last 26 weeks
10 a week over the last six months (or since the feed's oldest post)
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AI and agents (87), Infrastructure (34), Machine learning (18) in the last six months.
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Official checkSep 25
not reviewed to official: on nvidia.com