Reading room.
Engineering blogs & AI research
A reading room from engineering teams and AI labs: titles, short feed excerpts, and links to the originals. For my own essays, explore the writing archive.
- 1359 articles tracked
- 37 sources
- Refresh scheduled daily
Sources
Agent Substrate: Advancing Kubernetes for Agents at Scale
AI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response. Kubernetes wasn’t built for that. Running one Pod per...
Manage Kubernetes Clusters for GPU Infrastructure with NVIDIA Kubernetes Engine
NVIDIA is announcing two open source projects for building and operating managed Kubernetes services on GPU infrastructure—NVIDIA Kubernetes Engine (NVKE) and...
Running AV Reasoning Models Within Onboard Compute and Latency Limits
Autonomous vehicle (AV) developers need reasoning models that can operate within the compute and latency constraints of an onboard system. Larger models provide...
Start and Restore GPU Workloads in Seconds with NVIDIA Snapshot
NVIDIA Snapshot, a new open-source component of the NVIDIA DSX AI Factory Platform, makes initialized GPU workloads reusable across inference...
Make a Robot Arm Feel Weightless with NVIDIA Holoscan 5
Picture guiding a robot arm by hand while its motors supply just enough torque to support its weight and keep the robot upright. This simple interaction, a...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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,...
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,...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 1
Deploying a trained neural network involves more than translating each operator into a GPU kernel. It is a compilation problem with many legitimate answers. For...
Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 2
The Global Performance Tuner is a feature for automatic performance tuning released in NVIDIA TensorRT 11.1. It provides automatic, accuracy-aware and...
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...
How to Read Data into GPU Memory in Python with NVIDIA GPUDirect Storage
GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI...
Running Frontier-Class AI Models Across 8 NVIDIA DGX Spark Nodes
AI developers aren’t just running a single model anymore—they’re orchestrating multiple models in parallel, chaining inference calls, and experimenting...
Achieve Productive AI Capacity Faster and More Predictably with NVIDIA DSX POD
AI factories turn energy into intelligence. But before an AI workload can produce its first token, operators must bring an interconnected system of compute,...
Design Proteins with RFD4-Proteina Using Reward-Guided Inference-Time Search
Protein design is a search problem. Choose amino acids for an eight-residue protein sequence and more than 25 billion combinations are possible. Choose amino...
Developing an AI Factory Digital Twin with an Agent-Assisted Workflow
Building an AI factory digital twin requires developers to bring site plans, equipment assets, and engineering requirements into a consistent model that teams...
How NVIDIA OpenShell Reference Architecture Secures and Governs AI Agent Workloads at Scale
Autonomous agents can work for hours on complex tasks, accessing files, tools, the internet, and spawning other agents to collaborate with. Executing complex...
Inside NVIDIA Sentry: Secure AI Agents at Runtime
An AI agent can complete its assignment and still cross a security boundary. Recent incidents show that agents can circumvent security controls while using...
Accelerating Mamba-2 with High-Performance Forward Pass Kernels in NVIDIA CUDA Tile
NVIDIA CUDA Tile (cuTile) is a Python-based DSL and parallel programming model for NVIDIA GPUs that automatically leverages advanced hardware capabilities to...
Code, Test, and Deploy on One Machine with NVIDIA RTX Spark
AI agents are changing how software gets built. Developers have gone from writing every line of code by hand to directing agents that write most of the code. As...
NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
Always-on agents are taking on longer, more complex enterprise workflows. A single request can involve reading a document, checking information across sources,...
Scale Agentic Coding Workflows with Meta’s Muse Spark Open on NVIDIA GB300 NVL72
Meta launches Muse Spark Open, a coding-optimized 816B mixture-of-experts (MoE) model with 42B active parameters and a 1M token context window. Trained on...
Optimizing Agent Reliability and Tool Calling Efficiency for Chip Design Using NVIDIA NeMo Gym
AI agents offer remarkable productivity gains in complex chip design workflows, but their tendency to generate plausible yet incorrect artifacts makes...
NVIDIA Run:ai Is Now Open Source as Part of NVIDIA DSX AI Factory Platform
NVIDIA is making the full NVIDIA Run:ai platform available as open source software, giving customers, developers, and ecosystem partners access to...
How to Rewrite Apache Spark UDFs for GPU Acceleration with AI Agents
Enterprise applications have become increasingly demanding. Agentic AI has compounded the need for vast amounts of enterprise data with frequent, complex...
ReplaySSM: Rethinking State Storage for Faster Hybrid-Model Decoding
Hybrid models interleave state-space model (SSM) layers with a few attention layers. NVIDIA Nemotron 3, Qwen3.5 through Qwen3.8, Kimi-K3, and GLM-5.3-Flash are...
5 Steps to Create SimReady Assets for Robotics with Frontier AI Models
Preparing CAD assets for robotics simulation requires more than converting geometry to OpenUSD: developers must configure and validate materials, collision...
Building Reliable Data Analytics Agents: Lessons from the KDD Cup
The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition with a system built around a simple idea of making an agent's harness smaller,...
Faster Scientific Image Analysis with NVIDIA cuPhoton
Observatories and telescopes, lasers and X-ray light sources, and other high-throughput instruments generate image data faster than CPU-bound pipelines can...