# Top Stories

Published articles for Top Stories.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs

DevFeed: [CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs](<https://devfeed.tech/articles/cuda-toolkit-13-4-adds-windows-on-arm-support-and-greater-control-over-shared-gpus-6789.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/cuda-toolkit-13-4-adds-windows-on-arm-support-and-greater-control-over-shared-gpus/>)

Author: Jonathan Bentz

Published: 2026-09-09T20:24:12Z

Content type: release

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [CUDA](<https://devfeed.tech/topics/cuda.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>)

Tags: [cli](<https://devfeed.tech/tags/cli.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-tile](<https://devfeed.tech/tags/cuda-tile.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [nsight-tools-compute](<https://devfeed.tech/tags/nsight-tools-compute.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [release](<https://devfeed.tech/tags/release.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [windows](<https://devfeed.tech/tags/windows.md>)

### AI overview

CUDA Toolkit 13.4 adds Windows on Arm support, preview support for the NVIDIA Rubin GPU architecture, and new GPU-sharing controls through MPS V3. It also introduces CUDA Compute Fabric Transport for data movement across NVIDIA NVLink fabric.

### Source excerpt

Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software...

## Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron

DevFeed: [Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron](<https://devfeed.tech/articles/building-an-adaptive-agentic-cybersecurity-system-with-nvidia-nemotron-6770.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/building-an-adaptive-agentic-cybersecurity-system-with-nvidia-nemotron/>)

Author: Michelle Horton

Published: 2026-09-01T17:00:04Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [models](<https://devfeed.tech/tags/models.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [security-for-ai](<https://devfeed.tech/tags/security-for-ai.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [testing](<https://devfeed.tech/tags/testing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [traces](<https://devfeed.tech/tags/traces.md>), [trustworthy-ai-cybersecurity](<https://devfeed.tech/tags/trustworthy-ai-cybersecurity.md>)

### AI overview

The article describes an agentic cybersecurity system that uses red and blue agents to continuously test attacks, analyze telemetry, generate detections, and retest them in an isolated representative environment. It discusses NVIDIA Nemotron models used with CrowdStrike SafeMind for defensive orchestration and detection generation.

### Source excerpt

AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons. Security teams are beginning to...

## NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure

DevFeed: [NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure](<https://devfeed.tech/articles/nvidia-nvlink-fusion-brings-nvhbm-to-next-generation-ai-infrastructure-6903.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nvlink-fusion-brings-nvhbm-to-next-generation-ai-infrastructure/>)

Author: Farshad Ghodsian

Published: 2026-08-26T21:06:58Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [NVLink](<https://devfeed.tech/topics/nvlink.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [integration](<https://devfeed.tech/tags/integration.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [scale](<https://devfeed.tech/tags/scale.md>), [support](<https://devfeed.tech/tags/support.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

NVIDIA NVLink Fusion connects custom XPUs and CPUs to NVIDIA's AI infrastructure platform, while NVHBM provides validated HBM base-die technology intended to increase memory bandwidth, save package area, and reduce power consumption. The article describes benefits for training and large-scale inference, including up to 30% more memory bandwidth per stack than standard HBM4e.

### Source excerpt

AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads,...

## Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding

DevFeed: [Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding](<https://devfeed.tech/articles/experiment-with-qwen3-8-flash-next-on-nvidia-gb300-nvl72-for-agentic-coding-6819.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/experiment-with-qwen3-8-flash-next-on-nvidia-gb300-nvl72-for-agentic-coding/>)

Author: Michelle Horton

Published: 2026-08-26T17:07:12Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [qwen](<https://devfeed.tech/topics/qwen.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [TensorRT-LLM](<https://devfeed.tech/topics/tensorrt-llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [rust-ai](<https://devfeed.tech/topics/rust-ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This NVIDIA developer article introduces Qwen3.8-Flash-Next, a multimodal mixture-of-experts model released by Alibaba for experimentation and evaluation. It explains the model's long-context hybrid architecture, including Gated DeltaNet and Qwen Sparse Attention, and discusses reported efficiency improvements for million-token workloads. The article also covers inference support through SGLang, vLLM, TensorRT-LLM, and NVIDIA NeMo, plus performance on the NVIDIA GB300 NVL72 platform.

### Source excerpt

Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate. It's...

## NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories

DevFeed: [NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories](<https://devfeed.tech/articles/nvidia-bluefield-4-powers-new-scale-in-network-infrastructure-for-agentic-ai-factories-6889.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-bluefield-4-powers-new-scale-in-network-infrastructure-for-agentic-ai-factories/>)

Author: Michelle Horton

Published: 2026-08-24T15:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [bluefield-dpu](<https://devfeed.tech/tags/bluefield-dpu.md>), [connectx](<https://devfeed.tech/tags/connectx.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

NVIDIA describes Scale-In network infrastructure for agentic AI factories, centered on BlueField-4, DOCA, and Spectrum-X Ethernet. The architecture is intended to accelerate networking, storage, security, data movement, tenant isolation, and infrastructure operations as AI compute scales.

### Source excerpt

Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,...

## Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU

DevFeed: [Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU](<https://devfeed.tech/articles/solving-agentic-ai-fleet-challenges-with-nvidia-vera-cpu-6941.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/solving-agentic-ai-fleet-challenges-with-nvidia-vera-cpu/>)

Author: Michelle Horton

Published: 2026-08-24T15:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [latency](<https://devfeed.tech/tags/latency.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [systems](<https://devfeed.tech/tags/systems.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

The article argues that AI-agent CPU fleets should use a balanced design that combines strong per-core performance for latency-bound sequential work with enough concurrency for intermittent parallel bursts. It presents NVIDIA Vera CPU as designed for those agentic workload patterns.

### Source excerpt

AI factories are interconnected systems where fleet economics depend on how efficiently the entire stack converts power and capital into completed agent tasks....

## NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents

DevFeed: [NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents](<https://devfeed.tech/articles/nvidia-avo-reaches-100-on-arc-agi-3-demonstrating-a-frontier-level-general-purpose-architecture-for-long-horizon-autonomous-agents-6887.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-avo-reaches-100-on-arc-agi-3-demonstrating-a-frontier-level-general-purpose-architecture-for-long-horizon-autonomous-agents/>)

Author: Tanya Lenz

Published: 2026-08-21T13:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-agents](<https://devfeed.tech/tags/autonomous-agents.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [coding](<https://devfeed.tech/tags/coding.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-research](<https://devfeed.tech/tags/nvidia-research.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [trustworthy-ai-cybersecurity](<https://devfeed.tech/tags/trustworthy-ai-cybersecurity.md>)

### AI overview

NVIDIA introduces AVO, a general-purpose coding-agent architecture intended for sustained autonomous work on long, multistep tasks. The article describes its use in GPU-kernel optimization and its adaptation to the ARC-AGI-3 benchmark through different task-specific tools and evaluation.

### Source excerpt

A frontier language model is only one component of an AI agent. The surrounding agent system--often called a harness--determines how the model receives...

## Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72

DevFeed: [Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72-6938.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72/>)

Author: Michelle Horton

Published: 2026-08-12T18:23:13Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cache](<https://devfeed.tech/tags/cache.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model](<https://devfeed.tech/tags/model.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [routing](<https://devfeed.tech/tags/routing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

The article describes serving Alibaba's open-weight Qwen3.8-2.4T-A95B model on NVIDIA GB300 NVL72 systems for large-scale reasoning and agentic workloads.

### Source excerpt

Alibaba released the open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model, bringing near-frontier capabilities to the open...

## NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents

DevFeed: [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents](<https://devfeed.tech/articles/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents-6899.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/>)

Author: Tanya Lenz

Published: 2026-08-11T13:01:07Z

Content type: release

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [featured](<https://devfeed.tech/tags/featured.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nemoclaw](<https://devfeed.tech/tags/nemoclaw.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [routing](<https://devfeed.tech/tags/routing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

NVIDIA introduces Nemotron 3.5 Lightning, an open 30B MoE model with 3B active parameters for fast, high-volume execution in long-running AI agents. It also presents NeMo Switchyard for routing tasks to appropriate models.

### Source excerpt

Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning...

## Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard

DevFeed: [Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard](<https://devfeed.tech/articles/route-ai-agent-workloads-across-models-with-nvidia-nemo-switchyard-6930.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/route-ai-agent-workloads-across-models-with-nvidia-nemo-switchyard/>)

Author: Michelle Horton

Published: 2026-08-11T13:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [applications](<https://devfeed.tech/tags/applications.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [classification](<https://devfeed.tech/tags/classification.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [cost](<https://devfeed.tech/tags/cost.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [math](<https://devfeed.tech/tags/math.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

The article explains how NVIDIA NeMo Switchyard routes AI-agent tasks to different models according to task requirements, capabilities, cost, and latency.

### Source excerpt

Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one...

## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA

DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA](<https://devfeed.tech/articles/run-local-agentic-ai-workflows-with-meta-s-muse-glimmer-on-nvidia-6932.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-local-agentic-ai-workflows-with-metas-muse-glimmer-on-nvidia/>)

Author: Michelle Horton

Published: 2026-08-10T13:27:19Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Jetson](<https://devfeed.tech/topics/jetson.md>), [Automation](<https://devfeed.tech/topics/automation.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [automation](<https://devfeed.tech/tags/automation.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [dgx-station](<https://devfeed.tech/tags/dgx-station.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [jetson](<https://devfeed.tech/tags/jetson.md>), [local-ai](<https://devfeed.tech/tags/local-ai.md>), [nemoclaw](<https://devfeed.tech/tags/nemoclaw.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data.

### Source excerpt

Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI...

## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super

DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super](<https://devfeed.tech/articles/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alpamayo-2-super-6828.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alpamayo-2-super/>)

Author: Elizabeth Goodman

Published: 2026-08-04T15:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [automotive-transportation](<https://devfeed.tech/tags/automotive-transportation.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [customization](<https://devfeed.tech/tags/customization.md>), [data](<https://devfeed.tech/tags/data.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [drive](<https://devfeed.tech/tags/drive.md>), [driving](<https://devfeed.tech/tags/driving.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [generate](<https://devfeed.tech/tags/generate.md>), [generation](<https://devfeed.tech/tags/generation.md>), [github](<https://devfeed.tech/tags/github.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [learning](<https://devfeed.tech/tags/learning.md>), [models](<https://devfeed.tech/tags/models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [robot-navigation](<https://devfeed.tech/tags/robot-navigation.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows.

### Source excerpt

Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data...

## NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage

DevFeed: [NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage](<https://devfeed.tech/articles/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage-6914.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/>)

Author: Elizabeth Goodman

Published: 2026-08-03T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Security](<https://devfeed.tech/topics/security.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [bluefield-dpu](<https://devfeed.tech/tags/bluefield-dpu.md>), [cloud-apis](<https://devfeed.tech/tags/cloud-apis.md>), [cloud-networking](<https://devfeed.tech/tags/cloud-networking.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [compression](<https://devfeed.tech/tags/compression.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [doca](<https://devfeed.tech/tags/doca.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [featured](<https://devfeed.tech/tags/featured.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [security](<https://devfeed.tech/tags/security.md>), [software-defined-data-center](<https://devfeed.tech/tags/software-defined-data-center.md>), [storage](<https://devfeed.tech/tags/storage.md>), [systems](<https://devfeed.tech/tags/systems.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

NVIDIA presents benchmark results for the Vera BlueField-4 STX Storage Processor in AI-native storage workloads. The results describe faster encryption and decryption, recovery, integrity checking, compression and decompression, and multi-stage storage processing than an x86 CPU, with lower CPU and power overhead.

### Source excerpt

Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,...

## Six Agent Harness Capabilities for Higher Model Performance

DevFeed: [Six Agent Harness Capabilities for Higher Model Performance](<https://devfeed.tech/articles/six-agent-harness-capabilities-for-higher-model-performance-6940.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/six-agent-harness-capabilities-for-higher-model-performance/>)

Author: Michelle Horton

Published: 2026-07-27T09:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [Developer Tools](<https://devfeed.tech/topics/developer-tools.md>), [ai observability](<https://devfeed.tech/topics/ai-observability.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [featured](<https://devfeed.tech/tags/featured.md>), [framework](<https://devfeed.tech/tags/framework.md>), [llms](<https://devfeed.tech/tags/llms.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [openshell](<https://devfeed.tech/tags/openshell.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [security-for-ai](<https://devfeed.tech/tags/security-for-ai.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [trustworthy-ai-cybersecurity](<https://devfeed.tech/tags/trustworthy-ai-cybersecurity.md>)

### AI overview

The article presents NVIDIA Labs Object-Oriented Agents (NOOA), a Python-based framework for building AI agents as single classes. It outlines six harness capabilities intended to improve model performance, including typed interfaces, live object references, code-based actions, programmable loops, explicit state, and model-callable APIs.

### Source excerpt

Building a great AI agent isn't just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes...

## NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding

DevFeed: [NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding](<https://devfeed.tech/articles/nvidia-nemotron-3-ultra-leads-open-models-on-accuracy-and-efficiency-in-agentic-rtl-coding-6901.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-leads-open-models-on-accuracy-and-efficiency-in-agentic-rtl-coding/>)

Author: Nirmal Kumar Juluru

Published: 2026-07-27T00:45:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [coding](<https://devfeed.tech/topics/coding.md>), [Verilog](<https://devfeed.tech/topics/verilog.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [debugging](<https://devfeed.tech/topics/debugging.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Code generation](<https://devfeed.tech/topics/code-generation.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [automation](<https://devfeed.tech/tags/automation.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [chip-design](<https://devfeed.tech/tags/chip-design.md>), [code](<https://devfeed.tech/tags/code.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [coding](<https://devfeed.tech/tags/coding.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [featured](<https://devfeed.tech/tags/featured.md>), [hardware-semiconductor](<https://devfeed.tech/tags/hardware-semiconductor.md>), [llms](<https://devfeed.tech/tags/llms.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

NVIDIA's article presents ACE-RTL and Nemotron 3 Ultra as a combined approach to agentic RTL coding. The workflow generates Verilog, runs simulations and other EDA checks, analyzes failures, and iteratively refines designs while maintaining debugging context. The CVDP benchmark evaluates accuracy and efficiency on realistic RTL generation, modification, debugging, and verification tasks.

### Source excerpt

Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware...

## Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes

DevFeed: [Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes](<https://devfeed.tech/articles/start-customizing-nvidia-nemotron-3-nano-with-prime-intellect-lab-in-minutes-6942.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/start-customizing-nvidia-nemotron-3-nano-with-prime-intellect-lab-in-minutes/>)

Author: Chris Alexiuk

Published: 2026-07-23T16:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [rlvr](<https://devfeed.tech/topics/rlvr.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Python](<https://devfeed.tech/topics/python.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [coding](<https://devfeed.tech/tags/coding.md>), [customization](<https://devfeed.tech/tags/customization.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [getting-started](<https://devfeed.tech/tags/getting-started.md>), [math](<https://devfeed.tech/tags/math.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [python](<https://devfeed.tech/tags/python.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlvr](<https://devfeed.tech/tags/rlvr.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

This tutorial shows how to customize NVIDIA Nemotron 3 Nano with Prime Intellect Lab using reinforcement learning with verifiable rewards on a Python Math environment. It covers a baseline-training-reevaluation workflow and produces a downloadable LoRA adapter.

### Source excerpt

Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...

## Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72

DevFeed: [Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72-6939.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72/>)

Author: Kirthi Devleker

Published: 2026-07-21T18:30:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [networking](<https://devfeed.tech/topics/networking.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [collective](<https://devfeed.tech/tags/collective.md>), [communication](<https://devfeed.tech/tags/communication.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [train](<https://devfeed.tech/tags/train.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains how NVIDIA GB300 NVL72 achieved a world record for DeepSeek-V3 671B mixture-of-experts pre-training. It focuses on the communication demands of MoE models, including all-to-all traffic between GPUs, and the need for tightly coupled scale-up and predictable scale-out networking to sustain delivered training performance.

### Source excerpt

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...

## Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

DevFeed: [Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI](<https://devfeed.tech/articles/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai-6863.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/>)

Author: Eduardo Alvarez

Published: 2026-07-21T18:15:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [scale](<https://devfeed.tech/tags/scale.md>), [tensor-cores](<https://devfeed.tech/tags/tensor-cores.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [training](<https://devfeed.tech/tags/training.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

This article examines the NVIDIA Rubin GPU architecture and its co-designed Vera Rubin platform for agentic AI inference. It describes how Tensor Cores, HBM4 memory, the Transformer Engine, NVFP4 performance, cache, decoding, and scale-up systems address throughput, latency, long-context execution, and rack-scale deployment.

### Source excerpt

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....

## NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI

DevFeed: [NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI](<https://devfeed.tech/articles/nvidia-vera-cpu-olympus-cores-built-for-maximum-single-thread-performance-in-agentic-ai-6865.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-vera-cpu-olympus-cores-built-for-maximum-single-threaded-performance-in-agentic-ai/>)

Author: Praveen Menon

Published: 2026-07-21T18:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [cpu](<https://devfeed.tech/topics/cpu.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [code](<https://devfeed.tech/tags/code.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [sandboxes](<https://devfeed.tech/tags/sandboxes.md>), [tools](<https://devfeed.tech/tags/tools.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

The article explains how NVIDIA's Vera CPU and its Olympus cores are designed for the single-threaded performance, memory bandwidth, and predictable latency required by concurrent agentic AI workloads.

### Source excerpt

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with...

## Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance

DevFeed: [Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance](<https://devfeed.tech/articles/create-a-langchain-deep-agents-harness-profile-for-nvidia-nemotron-3-ultra-to-improve-performance-6784.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/create-a-langchain-deep-agents-harness-profile-for-nvidia-nemotron-3-ultra-to-improve-performance/>)

Author: Sean Lopp

Published: 2026-07-08T18:17:59Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [ai observability](<https://devfeed.tech/topics/ai-observability.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [customization](<https://devfeed.tech/tags/customization.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [profile](<https://devfeed.tech/tags/profile.md>), [python](<https://devfeed.tech/tags/python.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

A tutorial on creating and evaluating a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra. It uses benchmark-driven iteration to tune harness behavior and improve performance without fine-tuning the model.

### Source excerpt

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are...

## NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads

DevFeed: [NVIDIA Vera CPU Boosts AI Factory Throughput to Accelerate Agentic Workloads](<https://devfeed.tech/articles/nvidia-vera-cpu-boosts-ai-factory-throughput-to-accelerate-agentic-workloads-6910.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-vera-cpu-boosts-ai-factory-throughput-to-accelerate-agentic-workloads/>)

Author: Michelle Horton

Published: 2026-07-07T18:10:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Vera CPU](<https://devfeed.tech/topics/vera-cpu.md>), [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [cache](<https://devfeed.tech/tags/cache.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [featured](<https://devfeed.tech/tags/featured.md>), [inference](<https://devfeed.tech/tags/inference.md>), [model](<https://devfeed.tech/tags/model.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rl](<https://devfeed.tech/tags/rl.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>)

### AI overview

This NVIDIA developer article explains how the NVIDIA Vera CPU can improve AI factory throughput for agentic workloads. It emphasizes sustained per-core performance for CPU tasks between model steps, including tool calls, code execution, sandbox evaluations, data processing, orchestration, KV-cache coordination, and result handling. The article also describes how CPU performance affects reinforcement learning rollouts, user response time, and cached-context efficiency.

### Source excerpt

Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and...