# NVIDIA DGX Published articles for NVIDIA DGX. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX DevFeed: [Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-14T15:00:52Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Artificial Intelligence](), [NVIDIA RTX](), [Windows](), [GeForce](), [qwen](), [NVIDIA DGX](), [Cloud](), [GitHub](), [Slack](), [Google](), [Microsoft]() Tags: [agentic-ai](), [ai](), [ai-agents](), [cloud](), [dgx-spark](), [drive](), [geforce](), [github](), [google](), [local-ai](), [microsoft](), [nvidia-dgx](), [nvidia-rtx](), [open-source](), [qwen](), [rtx-pro](), [rtx-spark](), [slack](), [windows]() ### AI overview Perplexity is adding Portable Computer to its Windows app for compatible NVIDIA GeForce RTX PCs and NVIDIA RTX PRO Workstations. The local agent uses NVIDIA-accelerated models to plan multistep tasks, analyze files, and keep sensitive information on the device, while users can authorize cloud support for more advanced research and reasoning. ### Source excerpt As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information [...] ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things DevFeed: [Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-16T22:00:39Z Content type: opinion Language: en Sources: [Simon Willison]() Topics: [qwen](), [Large Language Model](), [Hardware](), [DGX Spark](), [NVIDIA DGX](), [SVG]() Tags: [ai](), [ai-2-235](), [ai-in-china](), [ai-in-china-108](), [coding-agents](), [coding-agents-248](), [cost](), [dgx-spark](), [generative-ai](), [generative-ai-1-981](), [llama-cpp](), [llama-cpp-29](), [llm](), [llm-reasoning](), [llm-reasoning-103](), [llm-release](), [llm-release-231](), [llms](), [llms-1-947](), [lm-studio](), [lm-studio-23](), [local-llms](), [local-llms-164](), [nvidia-dgx](), [nvidia-spark](), [nvidia-spark-6](), [pelican-riding-a-bicycle](), [pelican-riding-a-bicycle-142](), [pi](), [pi-6](), [qwen](), [qwen-61](), [reasoning](), [speed](), [svg]() ### AI overview Simon Willison evaluates Qwen 3.8 27B, a vision-capable 27-billion-parameter LLM that can run locally on suitable hardware. He finds that its default xhigh reasoning setting consumes substantial context and time, while adjusting the reasoning effort and increasing the context limit improves practicality. He also reports strong results generating an SVG locally. ### Source excerpt Friday's big release was Qwen 3.8 27B, an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba's Qwen research lab. I've been looking forward to this one: 27B is an excellent size for running a model on a reasonably specced laptop, and its predecessor Qwen 3.6 27B was impressive. Qwen's self-reported benchmarks for this model are eye-opening. They show a boost from both Qwen 3.6 27B and the closed-weight Qwen 3.7-Plus, which was one of Qwen's strongest models of any size as recently as May this year. It will be interesting to hear what independent benchmarks have to say about the model. I've been running the model on two different machines: my 128GB M5 Max MacBook Pro, and an NVIDIA DGX Spark. On both machines I'm running LM Studio and their 17GB Q4_K_M quantized build. I also tried using llama-server directly on the Spark. The default of extra high results in spectacular over-thinking Qwen's documentation describes the model as defaulting to xhigh for the reasoning effort, and the LM Studio GGUF I've been trying preserves that default: Qwen3.8 comes with official support for reasoning_effort, which can be used to adjust reasoning depth and control cost: xhigh (default): for complex tasks demanding thorough analysis medium: balancing accuracy and speed low: efficient reasoning optimizing for speed and cost This is a hilarious default. It's absolutely not a good way to run the model, especially on consumer hardware. I've been finding the results extremely entertaining. I quickly ran into problems with LM Studio's default context limit of 8,192 tokens - Qwen was using them all up thinking about even the most mundane of problems. I loaded the model with the full 262,144 maximum context length and that problem went away. Here's the pelican riding a bicycle SVG I got from my first attempt with that increased context length. It took 21 minutes to generate, using 22,276 reasoning tokens to produce 3,223 tokens of output. You can read the reasoning trace here. Th ## How to Choose Full-Stack Observability for NVIDIA AI Factories DevFeed: [How to Choose Full-Stack Observability for NVIDIA AI Factories]() Original publisher: [Read original article]() Author: Jorge Cardoso Published: 2026-08-12T16:13:47Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [observability](), [AI Infrastructure](), [NVIDIA DGX](), [InfiniBand](), [telemetry](), [dashboards](), [NCCL](), [Monitoring]() Tags: [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [distributed-training](), [featured](), [infiniband](), [infrastructure](), [monitoring](), [nccl](), [networking](), [networking-communications](), [nvidia-dgx](), [observability](), [operations](), [orchestration](), [performance](), [storage](), [systems](), [telemetry]() ### AI overview A practical guide to choosing a full-stack observability strategy for NVIDIA AI infrastructure. It explains how to connect telemetry across compute, networking, storage, orchestration, and applications, using an InfiniBand gray-failure example to show how degraded hardware and NCCL collective-operation delays can reduce distributed-training throughput. ### Source excerpt AI infrastructure spans multiple layers, from compute and networking to storage, orchestration, and applications. When performance degrades, identifying the... ## 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]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### 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... ## Running Low-Latency Analytical Workloads with GPU-Accelerated Presto on NVIDIA GB200 NVL72 DevFeed: [Running Low-Latency Analytical Workloads with GPU-Accelerated Presto on NVIDIA GB200 NVL72]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-08T16:05:25Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [benchmarking](), [SQL](), [Open Source](), [datasets](), [Nvidia](), [GPUDirect](), [NVIDIA DGX](), [NVLink](), [parquet](), [C++](), [Optimization]() Tags: [benchmarking](), [benchmarks](), [c-plus-plus](), [cache](), [communication](), [cpu](), [data](), [data-analytics](), [data-science](), [datasets](), [developer-tools-techniques](), [featured](), [gb200](), [gpu](), [gpudirect](), [latency](), [low-latency](), [multi-gpu](), [nvidia](), [nvidia-dgx](), [nvl72](), [open-source](), [performance]() ### AI overview This article presents GPU-accelerated Presto for low-latency analytical SQL workloads on large datasets. It compares single-node multi-GPU execution on NVIDIA DGX B200 and multinode NVIDIA GB200 NVL72 systems with CPU-based Presto, highlighting NVLink communication, GPUDirect Storage, cuDF algorithms, Parquet data, and benchmark results. ### Source excerpt Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance... ## Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop) DevFeed: [Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop)]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-06-22T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [DGX Spark](), [Ubuntu](), [NVIDIA DGX](), [GPU](), [NVLink](), [CUDA](), [Docker](), [Ansible](), [vllm](), [Blackwell](), [networking](), [GitHub]() Tags: [ai](), [ansible](), [blackwell](), [cuda](), [dell](), [dgx-spark](), [docker](), [github](), [gpu](), [homelab](), [networking](), [nvidia-dgx](), [nvlink](), [ubuntu](), [vllm]() ### AI overview A guide to replacing DGX OS with a minimized Ubuntu 24.04 Server installation on GB10 systems such as the NVIDIA DGX Spark and ASUS Ascent GX10. It explains the memory and power benefits of removing GNOME while retaining the NVIDIA drivers, CUDA, Docker, and NVIDIA Container Toolkit, and covers ConnectX-7 networking, dual-node setup, and Ansible automation. ### Source excerpt When you buy an NVIDIA DGX Spark or an ASUS Ascent GX10, it ships with DGX OS. DGX OS is NVIDIA's managed Ubuntu image, and it is fine - if you want a full GNOME desktop on an AI box. I did not want that. The GB10 has 128 GB of unified memory shared between the CPU and GPU over NVLink-C2C. Every gigabyte the OS and desktop environment consume is a gigabyte not available to your model. On the ... ## Running the Latest vLLM on the NVIDIA DGX Spark DevFeed: [Running the Latest vLLM on the NVIDIA DGX Spark]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [vllm](), [DGX Spark](), [Docker](), [Dockerfile](), [GitHub Actions](), [NCCL](), [CUDA](), [PyTorch](), [GitHub](), [NGC](), [GPU](), [Ubuntu]() Tags: [ai](), [cuda](), [dgx-spark](), [docker](), [docker-image](), [github](), [github-actions](), [gpu](), [homelab](), [nccl](), [ngc](), [nvidia-dgx](), [pytorch](), [self-hosted](), [ubuntu](), [vllm]() ### AI overview This tutorial presents a reproducible Docker image pipeline for running current vLLM releases on the NVIDIA DGX Spark's GB10 ARM64 platform. It explains why NVIDIA's NGC images can lag upstream and describes a GitHub Actions build on a self-hosted Spark runner with pinned inputs and generated lockfiles. ### Source excerpt When I built my local AI cluster on a pair of ASUS Ascent GX10s, the hard part was not serving a model. The hard part was getting a working vLLM image with current components. NVIDIA's official image was already over a month behind by the time I needed it, and waiting on their release schedule was not an option. If you saw that post, you know the GX10 is an ARM64 machine built around NVIDIA's ... ## Serverless Inference with Hugging Face and NVIDIA NIM DevFeed: [Serverless Inference with Hugging Face and NVIDIA NIM]() Original publisher: [Read original article]() Author: Philipp Schmid; Jeff Boudier Published: 2024-07-29T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [NVIDIA NIM](), [hugging face](), [Inference](), [model-serving](), [Serverless](), [DGX Cloud](), [Large Language Model](), [Generative AI](), [API](), [llama](), [Access Control](), [Claude]() Tags: [access-control](), [api](), [cloud](), [dgx-cloud](), [generative-ai](), [hardware](), [hugging-face](), [inference](), [llama](), [llm](), [nvidia](), [nvidia-dgx](), [nvidia-nim](), [partnerships](), [pricing](), [serverless]() ### AI overview Hugging Face announces a serverless NVIDIA NIM API on the Hugging Face Hub for Enterprise Hub organizations. The service provides API access to open generative AI models, including Llama and Mistral, on NVIDIA DGX Cloud infrastructure, with pay-as-you-go pricing and a guide for creating fine-grained organization tokens. The article notes that the service was deprecated and unavailable as of April 10, 2025. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.