# Developer Tools & Techniques

Published articles for Developer Tools & Techniques.

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## Translating CUDA Tile Operations from Python to Rust Using Agentic AI

DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI](<https://devfeed.tech/articles/translating-cuda-tile-operations-from-python-to-rust-using-agentic-ai-31486.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/translating-cuda-tile-operations-from-python-to-rust-using-agentic-ai/>)

Author: Tanya Lenz

Published: 2026-09-16T16:28:59Z

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: [CUDA Tile](<https://devfeed.tech/topics/cuda-tile.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [Agent Skill](<https://devfeed.tech/topics/agent-skill.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Compiler](<https://devfeed.tech/topics/compiler.md>)

Tags: [agent-skill](<https://devfeed.tech/tags/agent-skill.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-tile](<https://devfeed.tech/tags/cuda-tile.md>), [cutile](<https://devfeed.tech/tags/cutile.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [python](<https://devfeed.tech/tags/python.md>), [rust](<https://devfeed.tech/tags/rust.md>)

### AI overview

This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage.

### Source excerpt

cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to...

## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine](<https://devfeed.tech/articles/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine-21079.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine/>)

Author: Tanya Lenz

Published: 2026-09-14T16:39:15Z

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>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [Network](<https://devfeed.tech/topics/network.md>), [Python](<https://devfeed.tech/topics/python.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [mixtral](<https://devfeed.tech/topics/mixtral.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [communication](<https://devfeed.tech/tags/communication.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mixtral](<https://devfeed.tech/tags/mixtral.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [moe](<https://devfeed.tech/tags/moe.md>), [networks](<https://devfeed.tech/tags/networks.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [router](<https://devfeed.tech/tags/router.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement.

### Source excerpt

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...

## How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra

DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra](<https://devfeed.tech/articles/how-full-stack-nim-optimizations-deliver-2-5x-more-users-on-nemotron-3-ultra-6840.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-full-stack-nim-optimizations-deliver-2-5x-more-users-on-nemotron-3-ultra/>)

Author: Elizabeth Goodman

Published: 2026-09-10T16:55:32Z

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: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [build-ai-agents](<https://devfeed.tech/tags/build-ai-agents.md>), [cache](<https://devfeed.tech/tags/cache.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.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>), [mamba](<https://devfeed.tech/tags/mamba.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nim](<https://devfeed.tech/tags/nim.md>), [nvidia-blackwell](<https://devfeed.tech/tags/nvidia-blackwell.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

### AI overview

The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure.

### Source excerpt

Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as...

## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving](<https://devfeed.tech/articles/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving-6945.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving/>)

Author: Tanya Lenz

Published: 2026-09-09T20:31:04Z

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: [Optimization](<https://devfeed.tech/topics/optimization.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [computer-vision-video-analytics](<https://devfeed.tech/tags/computer-vision-video-analytics.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

### AI overview

This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time.

### Source excerpt

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill...

## 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...

## Introducing CUDA Rust: Two Tracks for Writing GPU Kernels

DevFeed: [Introducing CUDA Rust: Two Tracks for Writing GPU Kernels](<https://devfeed.tech/articles/introducing-cuda-rust-two-tracks-for-writing-gpu-kernels-6871.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/introducing-cuda-rust-two-tracks-for-writing-gpu-kernels/>)

Author: Elizabeth Goodman

Published: 2026-09-08T12:00:00Z

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: [GPU](<https://devfeed.tech/topics/gpu.md>), [Code generation](<https://devfeed.tech/topics/code-generation.md>), [toolchain](<https://devfeed.tech/topics/toolchain.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>)

Tags: [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-tile](<https://devfeed.tech/tags/cuda-tile.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [nemo-retriever](<https://devfeed.tech/tags/nemo-retriever.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [programming-languages-compilers](<https://devfeed.tech/tags/programming-languages-compilers.md>), [rust](<https://devfeed.tech/tags/rust.md>), [toolchains](<https://devfeed.tech/tags/toolchains.md>)

### AI overview

NVIDIA introduces CUDA Rust for writing GPU kernels natively in Rust and compiling them to PTX. The article explains the SIMT and Tile programming tracks, recommends Tile as the default, and describes cuda-oxide's Rust-based code-generation path.

### Source excerpt

In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and...

## Building a Memory-Driven Agent with NVIDIA NemoClaw

DevFeed: [Building a Memory-Driven Agent with NVIDIA NemoClaw](<https://devfeed.tech/articles/building-a-memory-driven-agent-with-nvidia-nemoclaw-6768.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/building-a-memory-driven-agent-with-nvidia-nemoclaw/>)

Author: Tanya Lenz

Published: 2026-09-04T18:04:55Z

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: [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [Markdown](<https://devfeed.tech/topics/markdown.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [build-ai-agents](<https://devfeed.tech/tags/build-ai-agents.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [llms](<https://devfeed.tech/tags/llms.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nemoclaw](<https://devfeed.tech/tags/nemoclaw.md>), [openshell](<https://devfeed.tech/tags/openshell.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [retrieval-augmented-generation-rag](<https://devfeed.tech/tags/retrieval-augmented-generation-rag.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

This tutorial describes building a memory-driven AI agent with NVIDIA NemoClaw for enterprise work. It presents a structured self model, separates evidence from derived knowledge and governed execution, and emphasizes retrieval, user corrections, security, and authorization.

### Source excerpt

Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it...

## How to Carry User Identity Across Federated Kubernetes and AI Platforms

DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms](<https://devfeed.tech/articles/how-to-carry-user-identity-across-federated-kubernetes-and-ai-platforms-6845.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-to-carry-user-identity-across-federated-kubernetes-and-ai-platforms/>)

Author: Elizabeth Goodman

Published: 2026-09-03T22:36:02Z

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 Platform](<https://devfeed.tech/topics/ai-platform.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ai-platforms-deployment](<https://devfeed.tech/tags/ai-platforms-deployment.md>), [api](<https://devfeed.tech/tags/api.md>), [aws](<https://devfeed.tech/tags/aws.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [data](<https://devfeed.tech/tags/data.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [identity](<https://devfeed.tech/tags/identity.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [software-defined-data-center](<https://devfeed.tech/tags/software-defined-data-center.md>)

### AI overview

The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application.

### Source excerpt

Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook...

## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough

DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough](<https://devfeed.tech/articles/the-modern-cuda-toolbox-in-practice-a-step-by-step-optimization-walkthrough-6944.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/the-modern-cuda-toolbox-in-practice-a-step-by-step-optimization-walkthrough/>)

Author: Elizabeth Goodman

Published: 2026-09-02T17:15:57Z

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: [CUDA](<https://devfeed.tech/topics/cuda.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [toolchain](<https://devfeed.tech/topics/toolchain.md>), [debug](<https://devfeed.tech/topics/debug.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [debug](<https://devfeed.tech/tags/debug.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [image-processing](<https://devfeed.tech/tags/image-processing.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [memory](<https://devfeed.tech/tags/memory.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

### AI overview

A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers.

### Source excerpt

NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing...

## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference](<https://devfeed.tech/articles/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference-6781.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference/>)

Author: Tanya Lenz

Published: 2026-09-02T16:04:19Z

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: [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [batch](<https://devfeed.tech/tags/batch.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula.

### Source excerpt

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and...

## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science

DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science](<https://devfeed.tech/articles/run-nvidia-bionemo-nim-microservices-for-protein-structure-prediction-in-claude-science-6934.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/run-nvidia-bionemo-nim-microservices-for-protein-structure-prediction-in-claude-science/>)

Author: Michelle Horton

Published: 2026-08-31T16:30:00Z

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 research agents](<https://devfeed.tech/topics/ai-research-agents.md>), [OpenSSH](<https://devfeed.tech/topics/openssh.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [bionemo](<https://devfeed.tech/tags/bionemo.md>), [claude](<https://devfeed.tech/tags/claude.md>), [code](<https://devfeed.tech/tags/code.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [drug-discovery](<https://devfeed.tech/tags/drug-discovery.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [nim](<https://devfeed.tech/tags/nim.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [science](<https://devfeed.tech/tags/science.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models.

### Source excerpt

Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next....

## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec

DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec](<https://devfeed.tech/articles/scale-av-perception-across-vehicle-platforms-with-nvidia-omniverse-nurec-6936.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/scale-av-perception-across-vehicle-platforms-with-nvidia-omniverse-nurec/>)

Author: Michelle Horton

Published: 2026-08-31T16:00:00Z

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: [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [data](<https://devfeed.tech/topics/data.md>), [configuration](<https://devfeed.tech/topics/configuration.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.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>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [omniverse](<https://devfeed.tech/tags/omniverse.md>), [performance](<https://devfeed.tech/tags/performance.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [post](<https://devfeed.tech/tags/post.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output.

### Source excerpt

A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle...

## Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect

DevFeed: [Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect](<https://devfeed.tech/articles/deploy-an-open-model-from-checkpoint-to-inference-in-two-commands-with-nvidia-tensorrt-model-connect-6798.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/deploy-an-open-model-from-checkpoint-to-inference-in-two-commands-with-nvidia-tensorrt-model-connect/>)

Author: Tanya Lenz

Published: 2026-08-28T17:06:28Z

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, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [api](<https://devfeed.tech/tags/api.md>), [applications](<https://devfeed.tech/tags/applications.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [integration](<https://devfeed.tech/tags/integration.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [model](<https://devfeed.tech/tags/model.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [tensorrt](<https://devfeed.tech/tags/tensorrt.md>)

### AI overview

The article explains NVIDIA TensorRT Model Connect, a collection of modifiable reference implementations for deploying supported open models from a Hugging Face ID or local checkpoint to native C++ inference. It describes a two-phase deployment bundle workflow, semantic and module-level C++ APIs, and custom GPU-kernel integration.

### Source excerpt

Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,...

## 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...

## Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo

DevFeed: [Restore LLM Inference Capacity in Seconds with Shadow Engine Recovery in NVIDIA Dynamo](<https://devfeed.tech/articles/restore-llm-inference-capacity-in-seconds-with-shadow-engine-recovery-in-nvidia-dynamo-6928.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/restore-llm-inference-capacity-in-seconds-with-shadow-engine-recovery-in-nvidia-dynamo/>)

Author: Michelle Horton

Published: 2026-08-25T20:57:54Z

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: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [sglang](<https://devfeed.tech/topics/sglang.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cuda-x](<https://devfeed.tech/tags/cuda-x.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [process](<https://devfeed.tech/tags/process.md>)

### AI overview

This article explains NVIDIA Dynamo's shadow engine recovery for LLM inference. A fully initialized shadow engine shares weights with the active engine and takes over after a process failure, reducing recovery from a 283-second cold restart to 7.3 seconds in the described two-worker deployment.

### Source excerpt

When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels,...

## CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access

DevFeed: [CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access](<https://devfeed.tech/articles/cuda-python-1-0-stable-apis-one-foundation-full-platform-access-6786.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/cuda-python-1-0-stable-apis-one-foundation-full-platform-access/>)

Author: Elizabeth Goodman

Published: 2026-08-25T15:00:00Z

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: [CUDA](<https://devfeed.tech/topics/cuda.md>), [Python](<https://devfeed.tech/topics/python.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [toolchain](<https://devfeed.tech/topics/toolchain.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [RAPIDS](<https://devfeed.tech/topics/rapids.md>), [releases](<https://devfeed.tech/topics/releases.md>)

Tags: [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [numba](<https://devfeed.tech/tags/numba.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [rapids](<https://devfeed.tech/tags/rapids.md>), [release](<https://devfeed.tech/tags/release.md>), [toolchain](<https://devfeed.tech/tags/toolchain.md>)

### AI overview

CUDA Python 1.0 makes the CUDA platform accessible from Python through stable, versioned components, including runtime access, low-level CUDA C API bindings, parallel algorithms, component discovery, and NVIDIA math libraries. The release establishes a shared foundation for Python GPU libraries and introduces semantic-versioning commitments for public APIs.

### Source excerpt

For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and...

## How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin

DevFeed: [How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin-6843.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

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: [d-matrix](<https://devfeed.tech/topics/d-matrix.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [gemma4](<https://devfeed.tech/topics/gemma4.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Cache](<https://devfeed.tech/topics/cache.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>), [ai](<https://devfeed.tech/tags/ai.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>), [cache](<https://devfeed.tech/tags/cache.md>), [collective](<https://devfeed.tech/tags/collective.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [speed](<https://devfeed.tech/tags/speed.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

NVIDIA Groq 3 LPX, paired with Vera Rubin NVL72, delivers high-interactivity AI inference for long-context workloads. A reported benchmark measured 3,431 output tokens per second on Gemma 4 31B with a 100K context.

### Source excerpt

NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the...

## How Generative Recommenders Are Redefining RecSys at Scale

DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale](<https://devfeed.tech/articles/how-generative-recommenders-are-redefining-recsys-at-scale-6841.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-generative-recommenders-are-redefining-recsys-at-scale/>)

Author: Elizabeth Goodman

Published: 2026-08-20T16:00:00Z

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: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [featured](<https://devfeed.tech/tags/featured.md>), [generative](<https://devfeed.tech/tags/generative.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [machine-learning-artificial-intelligence](<https://devfeed.tech/tags/machine-learning-artificial-intelligence.md>), [recommenders-personalization](<https://devfeed.tech/tags/recommenders-personalization.md>), [scale](<https://devfeed.tech/tags/scale.md>)

### AI overview

The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale.

### Source excerpt

Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and...

## Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator

DevFeed: [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator](<https://devfeed.tech/articles/evaluating-ai-agent-skill-performance-with-nvidia-skillevaluator-6817.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/evaluating-ai-agent-skill-performance-with-nvidia-skillevaluator/>)

Author: Michelle Horton

Published: 2026-08-19T16:00:00Z

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: [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [Security](<https://devfeed.tech/topics/security.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-skill](<https://devfeed.tech/tags/agent-skill.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>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [build-ai-agents](<https://devfeed.tech/tags/build-ai-agents.md>), [codex](<https://devfeed.tech/tags/codex.md>), [cursor](<https://devfeed.tech/tags/cursor.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [prompt-injection](<https://devfeed.tech/tags/prompt-injection.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [security](<https://devfeed.tech/tags/security.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [trustworthy-ai](<https://devfeed.tech/tags/trustworthy-ai.md>)

### AI overview

NVIDIA SkillEvaluator is an open-source evaluation layer for measuring how packaged skills affect AI-agent performance. It compares agent runs with and without a skill, using static validation, embedding-based distinctiveness checks, and live task evaluations in isolated sandboxes. The article reports benchmark results for more than 300 verified skills across over 30 NVIDIA products and describes integrations with Claude Code, Codex, Cursor, Skills.sh, ClawHub, and Hermes Hub.

### Source excerpt

AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding...

## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit

DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit](<https://devfeed.tech/articles/how-ai-coding-agents-can-unlock-materials-simulation-with-nvidia-alchemi-toolkit-6838.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-ai-coding-agents-can-unlock-materials-simulation-with-nvidia-alchemi-toolkit/>)

Author: Elizabeth Goodman

Published: 2026-08-18T18:00:00Z

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: [ALCHEMI](<https://devfeed.tech/topics/alchemi.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Python](<https://devfeed.tech/topics/python.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>)

Tags: [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [alchemi](<https://devfeed.tech/tags/alchemi.md>), [coding](<https://devfeed.tech/tags/coding.md>), [computational-chemistry-materials-science](<https://devfeed.tech/tags/computational-chemistry-materials-science.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>)

### AI overview

This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines.

### Source excerpt

Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the...

## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer

DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer](<https://devfeed.tech/articles/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer-6811.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer/>)

Author: Tanya Lenz

Published: 2026-08-17T18:12:48Z

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: [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Mamba](<https://devfeed.tech/topics/mamba.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compute](<https://devfeed.tech/tags/compute.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [model-optimizer](<https://devfeed.tech/tags/model-optimizer.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput.

### Source excerpt

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...

## 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...

## 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...

## 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...

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