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Received — 7 May 2026 ⏭ NVIDIA Technical Blog

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

7 May 2026 at 21:20
NVIDIA GB200 NVL72 introduces a fundamentally new way to build GPU clusters by extending NVIDIA NVLink coherence across an entire rack. This design enables...

NVIDIA GB200 NVL72 introduces a fundamentally new way to build GPU clusters by extending NVIDIA NVLink coherence across an entire rack. This design enables exascale performance, but it also changes the assumptions that many scheduling systems were built on. As a result, “rack-scale locality” becomes a hard constraint. When workloads cross domain boundaries, performance drops sharply…

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Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer

7 May 2026 at 21:18
This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints...

This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT. Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs.

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

7 May 2026 at 16:02
Decorative image.Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). When training slows down,...Decorative image.

Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). When training slows down, it becomes challenging to determine why and what to do next. A problem can span computation, communication, a specific rank, or underlying hardware. NVIDIA NCCL Inspector accelerates triaging by providing a lightweight and continuous…

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Received — 5 May 2026 ⏭ NVIDIA Technical Blog

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

5 May 2026 at 16:00
The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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Building for the Rising Complexity of Agentic Systems with Extreme Co-Design

5 May 2026 at 15:52
Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different.  Agents don't...

Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so…

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Received — 4 May 2026 ⏭ NVIDIA Technical Blog

Optimize Supply Chain Decision Systems Using NVIDIA cuOpt Agent Skills

4 May 2026 at 20:55
Decorative image.Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making....Decorative image.

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making. Traditionally, specialized operations research (OR) teams solved these problems by translating business questions into mathematical models. This process can take weeks and often produces fragile solutions that struggle to adapt when conditions…

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Received — 30 April 2026 ⏭ NVIDIA Technical Blog

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

30 April 2026 at 17:00
Decorative image.Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches...Decorative image.

Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches like super resolution, denoising, and neural rendering help real-time engines work more efficiently, offering new creative possibilities while keeping performance in mind. Unreal Engine 5 (UE5) has taken several steps in this direction…

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

30 April 2026 at 17:00
Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation 6X, and the second-generation...

Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation 6X, and the second-generation transformer model for NVIDIA Super Resolution. In this post, we’ll go over new technologies and resources to share with our game-developer community, including: At CES 2026, we introduced DLSS 4.5, extending its AI-driven…

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How to Build, Run, and Scale High-Quality Creator Workflows in ComfyUI

30 April 2026 at 16:16
Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks...

Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks that once took hours of manual effort into automated, repeatable pipelines. ComfyUI is an open-source, node-based creative tool that runs locally on NVIDIA RTX GPUs. It connects image generation, video synthesis, and language models into…

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

30 April 2026 at 15:54
A person working on code on their computer.NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores, and...A person working on code on their computer.

NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores, and matrix multiply-accumulate—rather than manually coordinating threads, warps, and shared memory. cuTile.jl brings the same tile-based approach to the dynamic programming language Julia. Users can write custom GPU kernels without dropping…

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Received — 29 April 2026 ⏭ NVIDIA Technical Blog

Powering AI Factories with NVIDIA Enterprise Reference Architectures

29 April 2026 at 16:41
A person working on a data center rack.The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and...A person working on a data center rack.

The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and real-time decision-making at scale, competitive advantage increasingly depends on the infrastructure that supports them. Success requires more than raw compute. It demands a scalable, predictable foundation that can orchestrate intelligent…

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Scaling Biomolecular Modeling Using Context Parallelism in NVIDIA BioNeMo

28 April 2026 at 19:00
For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,...

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragments—single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Now…

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

28 April 2026 at 16:01
Decoratove image showing mult-modal processing.Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on...Decoratove image showing mult-modal processing.

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented model chains—separate stacks for vision, audio, and text. This increases inference hops and orchestration complexity, driving up inference costs while weakening cross-modal context consistency. NVIDIA Nemotron 3 Nano Omni…

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24/7 Simulation Loops: How Agentic AI Keeps Subsurface Engineering Moving

28 April 2026 at 15:00
The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential...

The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential and time-intensive manual workflows. As data complexity grows, the gap between machine speed and human bandwidth has become a primary bottleneck. On-demand simulation workflows are currently hampered by both manual data overhead…

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Received — 25 April 2026 ⏭ NVIDIA Technical Blog

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

24 April 2026 at 23:29
DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient...

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million-token context inference. DeepSeek-V4-Pro is the largest model in the family, with 1.6T total parameters and 49B active parameters. DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, designed for higher-speed…

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