# DGX Spark Published articles for DGX Spark. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard DevFeed: [HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T23:15:57Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Local AI](), [Hardware](), [GPU](), [cpu](), [Large Language Model]() Tags: [consumer](), [cpu](), [deepseek](), [dgx-spark](), [gemma](), [gpu](), [hardware](), [local-ai](), [models](), [ollama](), [windows](), [workstation]() ### AI overview StorageReview previews HP's pre-production ZBook Ultra G3a 16, a local AI laptop with 192GB of unified memory and up to 160GB assignable to its integrated GPU. The article examines its hardware and planned testing while noting that shipping-hardware benchmarks are not yet available. ### Source excerpt HP's ZBook Ultra G1a 14 holds the Best for Large Models spot on our Best Laptops for Local AI leaderboard because its 128GB of unified memory, 96GB of it assignable to the GPU, loaded models no discrete-GPU laptop could touch. The new HP ZBook Ultra G3a 16 raises that pool to 192GB with up to The post HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard appeared first on StorageReview.com. ## 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 [...] ## DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs DevFeed: [DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-05T00:25:04Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [DGX Spark](), [Windows](), [boot](), [Blackwell](), [Arm]() Tags: [arm](), [blackwell](), [boot](), [consumer](), [dgx-spark](), [secure-boot](), [update](), [windows](), [workstation]() ### AI overview A firmware update for a DGX Spark cluster added the Windows UEFI CA certificate, suggesting groundwork for Windows support on the platform. The article explains the certificate's role in UEFI Secure Boot and notes that Windows on this hardware would still require drivers, an installer, and broader platform support. NVIDIA has announced October Windows PCs using the same GB10-class silicon under the RTX Spark name. ### Source excerpt NVIDIA used IFA in Berlin to put Windows at the center of its local AI story. The company's September 3 post covers a new Windows Agent framework for agents that run in the background under OS control, one-click Windows setups for OpenClaw and Nous Research's Hermes Agent, a beta of an inference router called NVIDIA The post DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs appeared first on StorageReview.com. ## Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026 DevFeed: [Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-03T16:00:59Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Inference](), [Hardware](), [Inference Performance](), [Mixture of Experts (MoE)]() Tags: [agentic-ai](), [agents](), [ai](), [ai-inference](), [announcements](), [artificial-intelligence](), [dgx-spark](), [hardware](), [inference](), [llama-cpp](), [local-ai](), [mixture-of-experts-moe](), [nvidia](), [nvidia-rtx](), [open-source](), [rtx-ai-garage](), [rtx-spark](), [video](), [vllm]() ### AI overview NVIDIA announces local-AI updates at IFA 2026, including agent tooling, faster local inference, RTX Spark Windows PCs, and locally runnable models for agentic, coding, and video-generation workloads. ### Source excerpt Frontier intelligence is going local. At IFA 2026, NVIDIA, Microsoft and its partners are teaming up to provide faster inference and new tools that make agents easier to set up and run locally on NVIDIA hardware. New compact NVIDIA RTX Spark Windows PCs are also coming in October to give AI enthusiasts, developers and creators [...] ## NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network DevFeed: [NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-03T16:00:00Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Local AI](), [GPU](), [Hardware]() Tags: [ai-agent](), [ai-agents](), [ai-inference](), [compute](), [content-creation-rendering](), [dgx-spark](), [edge-computing](), [gaming](), [geforce](), [gpu](), [hardware](), [inference](), [local](), [nvidia](), [open-source]() ### AI overview NVIDIA PAIR is a beta virtual inference router that distributes independent local inference requests across eligible machines on a home network. It works through compatible Ollama and LM Studio interfaces without requiring changes to an agent harness. ### Source excerpt AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents.... ## 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 ## 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]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-11T13:01:07Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Language models](), [AI Bots]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-agent](), [ai-agents](), [dgx-spark](), [featured](), [inference](), [low-latency](), [mixture-of-experts-moe](), [nemoclaw](), [nemotron](), [nvfp4](), [nvidia](), [open-source](), [orchestration](), [routing](), [top-stories]() ### 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... ## 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... ## NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning DevFeed: [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-27T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Ising](), [Quantum Computing](), [vlm](), [Nvidia](), [Open Source](), [Benchmark](), [benchmarking](), [NVFP4](), [DGX Spark]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [benchmark](), [benchmarking](), [data-science](), [dgx-spark](), [featured](), [ising](), [nvfp4](), [nvidia](), [open-source](), [quantum](), [quantum-computing](), [simulation-modeling-design](), [training-ai-models](), [vlm](), [vlms]() ### AI overview NVIDIA Ising Calibration 1.5 is an open-source vision-language model for interpreting quantum-processor diagnostics and recommending calibration actions. The article highlights zero-shot and in-context learning evaluation on QCalEval, plus an NVFP4-quantized version for local deployment. ### Source excerpt NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they... ## 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 ... ## Distributing LLM inference in DwarfStar DevFeed: [Distributing LLM inference in DwarfStar]() Original publisher: [Read original article]() Published: 2026-05-25T14:54:59Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Large Language Model](), [Local AI](), [Hardware](), [quantization](), [GPU](), [Nvidia](), [DGX Spark]() Tags: [dgx-spark](), [gpu](), [hardware](), [inference](), [llm](), [local](), [money](), [nvidia](), [performance](), [quantization]() ### AI overview The article discusses the cost and performance trade-offs of running large language models locally. It compares high-end NVIDIA systems, DGX Spark, and Apple hardware, and argues that an M5 Max laptop with 128GB of memory may currently offer the most practical option for local inference. ### Source excerpt High end NVIDIA cards, and the server and power needed to run them, cost a lot of money, especially if you plan to reach enough VRAM to run massive models. The alternative, so far, has been Apple hardware, or the DGX Spark that, even if severely limited because of memory bandwidth, still allows to run LLMs prompt processing (prefill) fast enough. The Mac Studio provided up to 512GB unified memory, a solution with modest memory bandwidth (but much better than the Spark) and compute at a price that was, after all, given the current situation, relatively fair. For instance, with DwarfStar the Mac Studio M3 Ultra 512GB can run DeepSeek v4 PRO at 150 t/s prefill and ~10-13 t/s decoding, not great but at a level that is usable for certain use cases. Even 2-bit quantized, DeepSeek v4 PRO resists very well, like Flash at the same quantization (today I made PRO write a C compiler, I'll publish the video soon). I would not consider a trivial fact to run a frontier model at home, with a ~12k total spending. One could expect this to get better and better, but the situation at the horizon appears cloudy. There is almost zero hope that NVIDIA setups will get less expensive, and even a small company can't afford to easily purchase and handle a small data center for local inference. At the same time the RAM shortage is making it not exactly likely that we will see a Mac Studio with an M5 Ultra, maybe 1.2T/s memory bandwidth and more compute (the M5 Max is already faster, compute wise, and has the Neural Accelerators inside each GPU core that help with certain models). So the current situation for local inference is that the best machine is probably a laptop. The M5 Max 128GB can run DeepSeek v4 Flash and Mimo V2.5, 2-bit quantized, at very decent prefill and decoding speeds. We are talking of ~500 t/s prefill and ~35-40t/s decoding speed, with a performance slope as the context size increases which is very acceptable. At the cost of 6-7k depending on the configuration, this is curr ## 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 ... ## DwarfStar 4 and the Future of Local AI Model Support DevFeed: [DwarfStar 4 and the Future of Local AI Model Support]() Original publisher: [Read original article]() Published: 2026-05-14T22:22:45Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Local AI](), [deepseek](), [Inference](), [Frontier Model](), [DGX Spark](), [GPU](), [Large Language Model]() Tags: [ai](), [deepseek](), [dgx-spark](), [frontier-model](), [gpu](), [inference](), [llms](), [local-ai]() ### AI overview The author reflects on DwarfStar 4's rapid popularity and attributes it to demand for a focused local AI experience, capable hardware, quantization, and recent local AI advances. They describe a plan to support the best practically fast open-weights model over time, with possible specialized variants for coding, legal, and medical use. ### Source excerpt I didn't expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the experience produced by the local AI movement in the latest years, that can be leveraged more promptly because of GPT 5.5 (otherwise you can't build DS4 in one week -- and even with all this help you need to know how to gently talk to LLMs). The last week was funny and also tiring, I worked 14 hours per day on average. My normal average is 4/6 since early Redis times, but the first few months of Redis were like that. So, what's next? Is this a project that starts and ends with DeepSeek v4 Flash? Nope, the model can change over time. The space will be occupied, in my vision, by the best current open weights model that is *practically fast* on a high end Mac or "GPU in a box" gear (like the DGX Spark and other similar setups). I bet that the next contender is DeepSeek v4 Flash itself, in the new checkpoint that will be released and, hopefully, a version specifically tuned for coding, and who knows, other expert-variants (not in the sense of MoE experts) maybe. For local inference, to have a ds4-coding, ds4-legal, ds4-medical models make a lot of sense, after all. You just load what you need depending on the question. It is the first time since I play with local inference (I play with it since the start) that I find myself using a local model for serious stuff that I would normally ask to Claude / GPT. This, I think, is really a big thing. It is also the first time that using vector steering I can enjoy an experience where the LLM can be used with more freedom. DeepSeek v4 Flash ## A few updates: the DGX Spark giveaway, new GPU inference slides, and my Vision AI Course DevFeed: [A few updates: the DGX Spark giveaway, new GPU inference slides, and my Vision AI Course]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-04-11T13:03:07Z Content type: opinion Language: en Sources: [Neural Bits]() Topics: [GPU](), [Artificial Intelligence](), [DGX Spark](), [Inference](), [Nvidia](), [FIRST]() Tags: [ai](), [ai-training](), [dgx-spark](), [gpu](), [inference](), [models](), [nvidia](), [presentation]() ### AI overview The author shares updates about the completed DGX Spark giveaway organized with NVIDIA and a GPU and AI slide deck that is still in development. The presentation aims to explain how GPUs are used at scale for AI training and inference. ### Source excerpt What I've been working on behind the scenes, and what I'm preparing next. ## Win an NVIDIA DGX Spark by joining me for Virtual NVIDIA GTC 2026 DevFeed: [Win an NVIDIA DGX Spark by joining me for Virtual NVIDIA GTC 2026]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-03-14T09:30:39Z Content type: article Language: en Sources: [Neural Bits]() Topics: [DGX Spark](), [Artificial Intelligence](), [Nvidia](), [GPU](), [Cloud]() Tags: [ai](), [cloud](), [dgx-spark](), [gpu](), [nvidia]() ### AI overview The article describes a Europe-only giveaway of one NVIDIA DGX Spark tied to attending a Virtual GTC 2026 session, newsletter subscription, and form submission. It also discusses the author's use of local GPU compute for iterating on AI systems. ### Source excerpt How to enter the giveaway - and what to expect if you're an AI engineer building on DGX Spark. ## Bringing Chrome to ARM64 Linux Devices DevFeed: [Bringing Chrome to ARM64 Linux Devices]() Original publisher: [Read original article]() Author: Chromium Blog (noreply@blogger.com) Published: 2026-03-12T20:01:00Z Content type: release Language: en Sources: [Chromium Blog]() Topics: [Chrome](), [Linux](), [Google](), [Extension](), [Chromium](), [Security](), [Artificial Intelligence](), [Malware](), [passwords](), [DGX Spark](), [data](), [real-time]() Tags: [ai](), [arm](), [chrome](), [chromium](), [data](), [dgx-spark](), [extensions](), [google](), [linux](), [malware](), [none](), [passwords](), [real-time](), [security]() ### AI overview Google announces the launch of Chrome for ARM64 Linux devices in Q2 2026, extending Chrome's availability across Arm-powered platforms. The release brings Google ecosystem integration, cross-device synchronization, extensions, translation, security protections, password management, and support for DGX Spark users. ### Source excerpt We're excited to announce that Google will launch Chrome for ARM64 Linux devices in Q2 2026, following the successful expansion of Chrome to Arm-powered macOS devices in 2020 and Arm-powered Windows devices in 2024. Launching Chrome for ARM64 Linux devices allows more users to enjoy the seamless integration of Google's most helpful services into their browser. This move addresses the growing demand for a browsing experience that combines the benefits of the open-source Chromium project with the Google ecosystem of apps and features. This release represents a significant undertaking to ensure that ARM64 Linux users receive the same secure, stable, and rich Chrome experience found on other platforms. Get the best of the Google ecosystem With Chrome, you are able to leverage the full power of the Google ecosystem, providing a more cohesive and feature-rich environment designed for convenience and cross-device continuity. By signing into a Google Account, your bookmarks, browsing history, and open tabs follow you across devices. You can easily access the best extensions the Chrome Web Store has to offer, without needing to use specialized tools or alter developer settings. And you can effortlessly translate webpages with a single click. Use the browser that is secure by design Chrome also offers the added benefit of Google's strongest security protections. Enabling Enhanced Protection in Safe Browsing offers real-time protection against phishing and malware by leveraging AI alongside Google's list of known threats. With the Google Pay integration you can easily and securely manage your payments, using Chrome autofill for an added level of convenience. And the Google Password Manager lets you securely store, generate, and sync complex passwords across all your devices, eliminating the need to memorize multiple logins. It goes beyond simple storage by actively monitoring your credentials for data breaches and providing "Password Checkup" alerts if any of your accounts are ## NVIDIA brings agents to life with DGX Spark and Reachy Mini DevFeed: [NVIDIA brings agents to life with DGX Spark and Reachy Mini]() Original publisher: [Read original article]() Author: Jeff Boudier; Nader Khalil; Alec Fong Published: 2026-01-05T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [DGX Spark](), [Nvidia](), [reachy](), [Artificial Intelligence](), [Nemotron](), [Deployment](), [inference-endpoints](), [Orchestration](), [Python](), [Open Source](), [Cloud](), [Serverless]() Tags: [agents](), [ai-agent](), [artificial-intelligence](), [code](), [deployment](), [dgx-spark](), [hardware](), [inference-endpoints](), [nvidia](), [open-source](), [orchestration](), [partnerships](), [python](), [reachy](), [robotics](), [serverless]() ### AI overview A step-by-step guide to building a desk-based AI companion with NVIDIA DGX Spark and Reachy Mini. It combines open reasoning and vision models, text-to-speech, Python, agent orchestration, and tool handlers, with options for local, cloud, and serverless deployment. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Unboxing the NVIDIA DGX Spark: First Impressions DevFeed: [Unboxing the NVIDIA DGX Spark: First Impressions]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-12-20T14:15:48Z Content type: article Language: en Sources: [Neural Bits]() Topics: [DGX Spark](), [Nvidia](), [AI Development](), [Hardware](), [GPU](), [Benchmark](), [Multi-GPU]() Tags: [ai](), [ai-development](), [benchmarks](), [dgx-spark](), [gpu](), [hardware](), [multi-gpu](), [nvidia]() ### AI overview A hands-on review of the NVIDIA DGX Spark covering its unboxing, hardware design, software stack, intended audience, and benchmarks. The article argues that DGX Spark is designed as a local, DGX-aligned AI development system for building, testing, and validating models before scaling to cloud or cluster infrastructure, rather than as a high-end GPU replacement. ### Source excerpt The hardware, The Software components, benchmarks, target audience, and what it can do for AI Developers.