# NVIDIA DGX

NVIDIA DGX is an enterprise AI platform that combines NVIDIA software, infrastructure, and expertise for AI development.

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## 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](<https://devfeed.tech/articles/perplexity-portable-computer-is-now-available-on-windows-powered-by-nvidia-rtx-21586.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/local-ai-perplexity-windows-pcs/>)

Author: Gerardo Delgado

Published: 2026-09-14T15:00:52Z

Content type: news

Language: en

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

Topics: [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>), [Windows](<https://devfeed.tech/topics/windows.md>), [GeForce](<https://devfeed.tech/topics/geforce.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Slack](<https://devfeed.tech/topics/slack.md>), [Google](<https://devfeed.tech/topics/google.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [drive](<https://devfeed.tech/tags/drive.md>), [geforce](<https://devfeed.tech/tags/geforce.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [local-ai](<https://devfeed.tech/tags/local-ai.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [rtx-pro](<https://devfeed.tech/tags/rtx-pro.md>), [rtx-spark](<https://devfeed.tech/tags/rtx-spark.md>), [slack](<https://devfeed.tech/tags/slack.md>), [windows](<https://devfeed.tech/tags/windows.md>)

### 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 [...]

## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer

DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer](<https://devfeed.tech/articles/asus-lays-out-a-full-ai-factory-platform-vera-rubin-nvl72-racks-stx-storage-and-a-governance-layer-12358.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/asus-lays-out-a-full-ai-factory-platform-vera-rubin-nvl72-racks-stx-storage-and-a-governance-layer>)

Author: Lyle Smith

Published: 2026-09-04T17:54:45Z

Content type: article

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [NVIDIA Vera](<https://devfeed.tech/topics/nvidia-vera.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [NVIDIA DSX](<https://devfeed.tech/topics/nvidia-dsx.md>), [ai-governance](<https://devfeed.tech/topics/ai-governance.md>), [Server](<https://devfeed.tech/topics/server.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-governance](<https://devfeed.tech/tags/ai-governance.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [asus](<https://devfeed.tech/tags/asus.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [nvidia-dsx](<https://devfeed.tech/tags/nvidia-dsx.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [object-storage](<https://devfeed.tech/tags/object-storage.md>), [platform](<https://devfeed.tech/tags/platform.md>), [storage](<https://devfeed.tech/tags/storage.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware.

### Source excerpt

ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer appeared first on StorageReview.com.

## 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](<https://devfeed.tech/articles/qwen-3-8-27b-is-excellent-but-it-defaults-to-wildly-overthinking-things-30498.md>)

Original publisher: [Read original article](<https://simonwillison.net/2026/Aug/16/qwen-38-27b/>)

Author: Simon Willison

Published: 2026-08-16T22:00:39Z

Content type: opinion

Language: en

Sources: [Simon Willison](<https://devfeed.tech/sources/simon-willison.md>)

Topics: [qwen](<https://devfeed.tech/topics/qwen.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [SVG](<https://devfeed.tech/topics/svg.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-2-235](<https://devfeed.tech/tags/ai-2-235.md>), [ai-in-china](<https://devfeed.tech/tags/ai-in-china.md>), [ai-in-china-108](<https://devfeed.tech/tags/ai-in-china-108.md>), [coding-agents](<https://devfeed.tech/tags/coding-agents.md>), [coding-agents-248](<https://devfeed.tech/tags/coding-agents-248.md>), [cost](<https://devfeed.tech/tags/cost.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [generative-ai-1-981](<https://devfeed.tech/tags/generative-ai-1-981.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llama-cpp-29](<https://devfeed.tech/tags/llama-cpp-29.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-reasoning](<https://devfeed.tech/tags/llm-reasoning.md>), [llm-reasoning-103](<https://devfeed.tech/tags/llm-reasoning-103.md>), [llm-release](<https://devfeed.tech/tags/llm-release.md>), [llm-release-231](<https://devfeed.tech/tags/llm-release-231.md>), [llms](<https://devfeed.tech/tags/llms.md>), [llms-1-947](<https://devfeed.tech/tags/llms-1-947.md>), [lm-studio](<https://devfeed.tech/tags/lm-studio.md>), [lm-studio-23](<https://devfeed.tech/tags/lm-studio-23.md>), [local-llms](<https://devfeed.tech/tags/local-llms.md>), [local-llms-164](<https://devfeed.tech/tags/local-llms-164.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvidia-spark](<https://devfeed.tech/tags/nvidia-spark.md>), [nvidia-spark-6](<https://devfeed.tech/tags/nvidia-spark-6.md>), [pelican-riding-a-bicycle](<https://devfeed.tech/tags/pelican-riding-a-bicycle.md>), [pelican-riding-a-bicycle-142](<https://devfeed.tech/tags/pelican-riding-a-bicycle-142.md>), [pi](<https://devfeed.tech/tags/pi.md>), [pi-6](<https://devfeed.tech/tags/pi-6.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [qwen-61](<https://devfeed.tech/tags/qwen-61.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [speed](<https://devfeed.tech/tags/speed.md>), [svg](<https://devfeed.tech/tags/svg.md>)

### 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](<https://devfeed.tech/articles/how-to-choose-full-stack-observability-for-nvidia-ai-factories-6847.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-to-choose-full-stack-observability-for-nvidia-ai-factories/>)

Author: Jorge Cardoso

Published: 2026-08-12T16:13:47Z

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: [observability](<https://devfeed.tech/topics/observability.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [featured](<https://devfeed.tech/tags/featured.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [observability](<https://devfeed.tech/tags/observability.md>), [operations](<https://devfeed.tech/tags/operations.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [storage](<https://devfeed.tech/tags/storage.md>), [systems](<https://devfeed.tech/tags/systems.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### 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](<https://devfeed.tech/articles/run-local-agentic-ai-workflows-with-meta-s-muse-glimmer-on-nvidia-6932.md>)

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

Author: Michelle Horton

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

Content type: article

Language: en

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

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

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

### AI overview

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

### Source excerpt

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

## 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](<https://devfeed.tech/articles/running-low-latency-analytical-workloads-with-gpu-accelerated-presto-on-nvidia-gb200-nvl72-6935.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/running-low-latency-analytical-workloads-with-gpu-accelerated-presto-on-nvidia-gb200-nvl72/>)

Author: Tanya Lenz

Published: 2026-07-08T16:05:25Z

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>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [GPUDirect](<https://devfeed.tech/topics/gpudirect.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [parquet](<https://devfeed.tech/topics/parquet.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>)

Tags: [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [cache](<https://devfeed.tech/tags/cache.md>), [communication](<https://devfeed.tech/tags/communication.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data](<https://devfeed.tech/tags/data.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpudirect](<https://devfeed.tech/tags/gpudirect.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### 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...

## How CockroachDB and IBM LinuxONE Rockhopper 5 Power Resilient AI Infrastructure

DevFeed: [How CockroachDB and IBM LinuxONE Rockhopper 5 Power Resilient AI Infrastructure](<https://devfeed.tech/articles/how-cockroachdb-and-ibm-linuxone-rockhopper-5-power-resilient-ai-infrastructure-23791.md>)

Original publisher: [Read original article](<https://cockroachlabs.com/blog/ibm-linuxone-rockhopper-5-ai-infrastructure>)

Author: Kyle Basile

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

Content type: article

Language: en

Sources: [Cockroach Labs](<https://devfeed.tech/sources/cockroach-labs.md>)

Topics: [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [CockroachDB](<https://devfeed.tech/topics/cockroachdb.md>), [ibm](<https://devfeed.tech/topics/ibm.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [availability](<https://devfeed.tech/tags/availability.md>), [cockroachdb](<https://devfeed.tech/tags/cockroachdb.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [consistency](<https://devfeed.tech/tags/consistency.md>), [customers](<https://devfeed.tech/tags/customers.md>), [data](<https://devfeed.tech/tags/data.md>), [digital](<https://devfeed.tech/tags/digital.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [failover](<https://devfeed.tech/tags/failover.md>), [financial](<https://devfeed.tech/tags/financial.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [identity](<https://devfeed.tech/tags/identity.md>), [identity-and-access](<https://devfeed.tech/tags/identity-and-access.md>), [identity-and-access-management](<https://devfeed.tech/tags/identity-and-access-management.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

The article explains how enterprise AI workloads increase requirements for continuous data availability, high concurrency, real-time decision-making, and elastic scaling. It presents IBM LinuxONE Rockhopper 5 and CockroachDB as infrastructure and data-platform components for resilient AI applications, including semantic retrieval, embeddings, agent memory, and transactional consistency.

### Source excerpt

Why does AI require a new approach to infrastructure?

## Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop)

DevFeed: [Ubuntu Server on the NVIDIA DGX Spark (Without the Desktop)](<https://devfeed.tech/articles/ubuntu-server-on-the-nvidia-dgx-spark-without-the-desktop-10683.md>)

Original publisher: [Read original article](<https://technotim.com/posts/ubuntu-gb10/>)

Author: Techno Tim

Published: 2026-06-22T13:00:00Z

Content type: tutorial

Language: en

Sources: [Techno Tim](<https://devfeed.tech/sources/techno-tim.md>)

Topics: [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>), [Ubuntu](<https://devfeed.tech/topics/ubuntu.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [Docker](<https://devfeed.tech/topics/docker.md>), [Ansible](<https://devfeed.tech/topics/ansible.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [networking](<https://devfeed.tech/topics/networking.md>), [GitHub](<https://devfeed.tech/topics/github.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ansible](<https://devfeed.tech/tags/ansible.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [dell](<https://devfeed.tech/tags/dell.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [docker](<https://devfeed.tech/tags/docker.md>), [github](<https://devfeed.tech/tags/github.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [homelab](<https://devfeed.tech/tags/homelab.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [ubuntu](<https://devfeed.tech/tags/ubuntu.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### 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 ...

## Mistral AI partners with NVIDIA to accelerate open frontier models

DevFeed: [Mistral AI partners with NVIDIA to accelerate open frontier models](<https://devfeed.tech/articles/mistral-ai-partners-with-nvidia-to-accelerate-open-frontier-models-7044.md>)

Original publisher: [Read original article](<https://mistral.ai/news/mistral-ai-and-nvidia-partner-to-accelerate-open-frontier-models/>)

Published: 2026-03-16T20:00:00Z

Content type: news

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [DGX Cloud](<https://devfeed.tech/topics/dgx-cloud.md>), [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [dgx-cloud](<https://devfeed.tech/tags/dgx-cloud.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>)

### AI overview

Mistral AI announces a partnership with NVIDIA and its founding membership in the NVIDIA Nemotron Coalition. The collaboration will develop open frontier AI models using Mistral AI's model expertise and NVIDIA's compute, development tools, and synthetic-data pipelines. The coalition's first initiative will support the NVIDIA Nemotron 4 family, while Mistral AI also releases Mistral Small 4 for developers, researchers, and organizations.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## Introducing Training Cluster as a Service - a new collaboration with NVIDIA

DevFeed: [Introducing Training Cluster as a Service - a new collaboration with NVIDIA](<https://devfeed.tech/articles/introducing-training-cluster-as-a-service-a-new-collaboration-with-nvidia-7373.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia-training-cluster>)

Author: Jeff Boudier; Arjuna; Simon Pagezy

Published: 2025-06-11T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [Hopper](<https://devfeed.tech/topics/hopper.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dgx-cloud](<https://devfeed.tech/tags/dgx-cloud.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hopper](<https://devfeed.tech/tags/hopper.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [libraries](<https://devfeed.tech/tags/libraries.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partners](<https://devfeed.tech/tags/partners.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Hugging Face and NVIDIA introduce Training Cluster as a Service, connecting organizations with GPU cluster capacity for AI model training. Organizations can request clusters sized for their needs and pay for the duration of training runs. The service combines NVIDIA Cloud Partners, NVIDIA DGX Cloud Lepton, and Hugging Face developer resources and open source libraries, with Hugging Face and NVIDIA coordinating procurement, pricing, provisioning, and setup.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.