NVIDIA Developer Blog
AI news source. Articles are auto-selected and adapted by Hamidun News editors.
Latest publications
NVIDIA Explains How to Build a Transaction Foundation Model for Financial Analytics
NVIDIA described an approach to creating a proprietary foundation model based on bank transaction data for fraud detection and more accurate analysis of customers' financial behavior.
NVIDIA XR AI: How the Company Helps Developers Build AI Agents for AR Glasses
NVIDIA described the infrastructure gap in developing AI agents for AR glasses and XR devices: the hardware is ready, but integrating live AI services remains complex.
NVIDIA Describes GQE — Platform for GPU-Accelerated Query Processing Engines
NVIDIA published technical material on GQE — a platform for designing GPU-accelerated query processing engines limited by memory bandwidth.
NVIDIA Developer Blog Explains AI Agent Reinforcement Learning Techniques
NVIDIA Developer Blog published a breakdown of reinforcement learning techniques for AI agents—from classic RLHF to new feedback-based model training approaches.
NVIDIA showed NuRec pipeline optimization via Nsight Developer Tools
NVIDIA explained how engineers accelerate neural network 3D reconstruction in Omniverse NuRec using Nsight Developer Tools profilers.
NVIDIA described nonuniform tensor parallelism for LLM training on GPUs
NVIDIA published in Developer Blog an analysis of the nonuniform tensor parallelism technique that increases goodput when training large language models on clusters of thousands of GPUs.
NVIDIA Showcases LangChain Deep Agents Profile for Nemotron 3 Ultra Model
NVIDIA released guidance on configuring a LangChain Deep Agents harness profile under its open Nemotron 3 Ultra model — proper configuration should bring accuracy closer to proprietary frontier models at lower cost.
NVIDIA Accelerates Presto Analytics Queries on GB200 NVL72 GPU Platform
NVIDIA showed GPU acceleration of the open SQL engine Presto on the GB200 NVL72 platform for low-latency analytics on very large datasets.
NVIDIA DSX OS: open operating system for AI factories of any scale
NVIDIA released a modular OS for managing mega data centers with AI accelerators, solving market fragmentation problem.
NVIDIA Vera CPU: New Standard for Agentic AI Tasks in Factories
The Vera processor is specially optimized for running agentic workloads in the era of scalable AI inference.
NVIDIA Cosmos 3: models for reasoning and actions in robots' physical world
NVIDIA presented Cosmos 3 — a set of foundational models for physical AI, including world models, reasoning models, and action models for robots and autonomous systems.
NVIDIA DGX Spark: Local AI Agents on Accelerated Models with Clustering
NVIDIA introduced support for local AI agents on DGX Spark with faster models and multi-node clustering capabilities for scalable workloads.
NVIDIA Alpamayo: Closed-Loop Model Training for Autonomous Vehicles
NVIDIA introduced Alpamayo—a platform for post-training autonomous vehicle models using a closed-loop cycle of simulation and real-world data.
NVIDIA DOCA: embedded security for AI agent infrastructure
NVIDIA presented DOCA — a platform embedded in network chips that provides built-in security guarantees for "AI factories" processing data in neural network computing.
NVIDIA NeMo generates synthetic financial data for AI
NVIDIA introduced a method for synthetic generation of financial data for LLM training, addressing the imbalance of rare events in real news.
NVIDIA BioNeMo Agent Toolkit: accelerating protein structure prediction
NVIDIA has introduced the BioNeMo Agent Toolkit to orchestrate AI agents that automate the full protein structure prediction and co-folding pipeline, accelerating pharmaceutical development.
LLMs and hardware: NVIDIA on balancing accuracy, throughput, and responsiveness
NVIDIA has published recommendations for LLM design that require simultaneous optimization of three parameters: accuracy, throughput, and response speed for the user.
NVIDIA Vera CPU Boosts Throughput of AI Factories for Agentic Workloads
NVIDIA explained how the Vera CPU processor removes delays between GPU inference steps and accelerates agentic multi-step pipelines in industrial AI factories.
NVIDIA Confidential Computing: protecting data during AI inference without sacrificing speed
NVIDIA explained how Confidential Computing protects enterprise data directly during GPU computation, without sacrificing performance.
NVIDIA BioNeMo lets you fine-tune biological AI models with LoRA in hours
NVIDIA has released BioNeMo Recipes — ready-made pipelines for fine-tuning the ESM2 and Evo 2 language models using the LoRA method. Specializing them for biological tasks now takes just a couple of GPUs.
NVIDIA speeds up LLM pretraining: NVFP4 on Blackwell paired with JAX and MaxText
NVIDIA showed how the NVFP4 format on Blackwell chips, paired with JAX and MaxText, reduces pretraining time and compute costs for frontier models.
NVIDIA JetPack 7.2: AI agents on edge devices with a single deployment command
NVIDIA released JetPack 7.2 for the Jetson platform — a single CLI command deploys a full agent stack with privacy and memory optimization.
NVIDIA TensorRT turns FP8 checkpoints into engines for fast inference
NVIDIA explained how to convert an FP8-quantized checkpoint into a TensorRT engine — a step that turns an optimized model into a real production tool.
NVIDIA: how to design battery energy storage systems for AI factories
NVIDIA explained how AI factories differ from conventional data centers in terms of power consumption and outlined the requirements for battery energy storage systems (BESS) for such facilities.
NVIDIA explained how to manage autonomous agents in enterprise AI factories
AI agents have moved far beyond chat — they work for hours with enterprise data and systems, and NVIDIA explained in detail how to build a secure, manageable environment for their deployment.
NVIDIA explained how to train transformers at reduced precision without losing quality
NVIDIA published a guide to optimizing transformer models with FP8 and BF16 — how to speed up training and reduce GPU memory usage without losing accuracy.
NVIDIA Introduces Halos — a Functional Safety System for AI-Powered Robots
NVIDIA Halos is a full functional-safety stack for physical AI: robots that work alongside humans in factories, warehouses, and hospitals.
NVIDIA DAQIRI: Real-Time AI Inference for High-Speed Data Acquisition
NVIDIA has introduced DAQIRI, a framework that embeds AI directly into the data stream from sensors and scientific instruments without delays.
NVIDIA releases guide to deploying AI-Q Blueprint on Oracle Cloud Infrastructure
NVIDIA has made production deployment of long-running AI agents possible: AI-Q Blueprint is now ready to run on Oracle Cloud with enterprise-grade security.
NVIDIA optimized BEV pooling on GPU for autonomous vehicles, robots, and spatial AI
NVIDIA published a guide to accelerating BEV pooling on GPU — a key operation for spatial perception in autonomous vehicles, robots, and spatial AI systems.