# Blackwell

Blackwell is a GPU architecture powering AI factories for the age of AI reasoning.

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## MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk

DevFeed: [MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk](<https://devfeed.tech/articles/msi-xpertstation-ws300-thermals-why-a-1-300w-gb300-doesn-t-throttle-on-a-desk-17439.md>)

Original publisher: [Read original article](<https://www.storagereview.com/review/msi-xpertstation-ws300-thermals-why-a-1300w-gb300-does-not-throttle-on-a-desk>)

Author: Brian Beeler

Published: 2026-09-14T19:41:35Z

Content type: article

Language: en

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

Topics: [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Grace CPU](<https://devfeed.tech/topics/grace-cpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [DGX Station](<https://devfeed.tech/topics/dgx-station.md>)

Tags: [blackwell](<https://devfeed.tech/tags/blackwell.md>), [consumer](<https://devfeed.tech/tags/consumer.md>), [dgx-station](<https://devfeed.tech/tags/dgx-station.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [heat](<https://devfeed.tech/tags/heat.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [review](<https://devfeed.tech/tags/review.md>), [workstation](<https://devfeed.tech/tags/workstation.md>)

### AI overview

The article explains why MSI's XpertStation WS300 can sustain a 1,300W GB300 Grace Blackwell Ultra Superchip without throttling on a desk. It attributes the thermal stability to cold plates covering the major heat-producing components, dual 360mm radiators, multiple fans, and a cooling loop rated above the system's nominal CPU and GPU load.

### Source excerpt

The most common question we got about the MSI XpertStation WS300 after our review coalesces around one key theme. The GB300 Grace Blackwell Ultra Superchip is a 1,300W part that normally lives in a liquid-cooled rack, so what happens to thermals when you put it in a tower? The concern is fair: a GB300 system The post MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk appeared first on StorageReview.com.

## HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge

DevFeed: [HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge](<https://devfeed.tech/articles/hp-zgx-fury-is-now-orderable-gb300-superchip-748gb-unified-memory-and-a-red-hat-ai-factory-plan-for-the-edge-12363.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/hp-zgx-fury-is-now-orderable-gb300-superchip-748gb-unified-memory-and-a-red-hat-ai-factory-plan-for-the-edge>)

Author: Brian Beeler

Published: 2026-09-09T19:35:12Z

Content type: news

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [DGX Station](<https://devfeed.tech/topics/dgx-station.md>), [Grace CPU](<https://devfeed.tech/topics/grace-cpu.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>)

Tags: [10gbe](<https://devfeed.tech/tags/10gbe.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [availability](<https://devfeed.tech/tags/availability.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [connectx](<https://devfeed.tech/tags/connectx.md>), [consumer](<https://devfeed.tech/tags/consumer.md>), [dgx-station](<https://devfeed.tech/tags/dgx-station.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [nvme](<https://devfeed.tech/tags/nvme.md>), [red-hat](<https://devfeed.tech/tags/red-hat.md>), [usb](<https://devfeed.tech/tags/usb.md>), [workstation](<https://devfeed.tech/tags/workstation.md>)

### AI overview

HP's ZGX Fury AI station is available to order with a GB300 Grace Blackwell Ultra Desktop Superchip, 748GB of unified memory, and up to 20 petaFLOPS of FP4 compute. HP positions it as a shared inference system for departments, factory floors, and branch offices, supported by a collaboration with Red Hat and NVIDIA to run Red Hat AI Factory with NVIDIA.

### Source excerpt

HP's ZGX Fury AI station is now available to order, and HP paired the availability news with a collaboration with Red Hat and NVIDIA to put Red Hat AI Factory with NVIDIA on top of it. The ZGX Fury is HP's take on NVIDIA's DGX Station design, built around the GB300 Grace Blackwell Ultra Desktop The post HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge appeared first on StorageReview.com.

## 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 ...

## GPU-Accelerated Remote Desktop on Linux from macOS - the Hard Way

DevFeed: [GPU-Accelerated Remote Desktop on Linux from macOS - the Hard Way](<https://devfeed.tech/articles/gpu-accelerated-remote-desktop-on-linux-from-macos-the-hard-way-10539.md>)

Original publisher: [Read original article](<https://technotim.com/posts/gpu-accelerated-rdp/>)

Author: Techno Tim

Published: 2026-04-13T13:00:00Z

Content type: tutorial

Language: en

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

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [Ubuntu](<https://devfeed.tech/topics/ubuntu.md>), [macOS](<https://devfeed.tech/topics/macos.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [homelab](<https://devfeed.tech/tags/homelab.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [macos](<https://devfeed.tech/tags/macos.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [ubuntu](<https://devfeed.tech/tags/ubuntu.md>)

### AI overview

This article documents the troubleshooting process for achieving GPU-accelerated H264 encoding over RDP from Ubuntu 24.04 ARM64 systems with NVIDIA GB10 Grace-Blackwell hardware to macOS. It compares xrdp's software-rendering path with GNOME Remote Desktop's CUDA-backed encoding and describes connection problems encountered with Microsoft's Windows App on macOS.

### Source excerpt

What started as "just set up RDP" turned into an all day rabbit hole about how Linux remote desktop actually works, why most of it doesn't work with NVIDIA on ARM64, and what it actually takes to get GPU-accelerated H264 encoding over RDP from an Ubuntu machine to a Mac. What I was trying to do I have two ASUS Ascent GX10 machines running Ubuntu 24.04. They are ARM64 systems built around th...

## DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300

DevFeed: [DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300](<https://devfeed.tech/articles/powering-the-next-leap-in-ai-gpu-droplets-accelerated-by-nvidia-hgxtm-b300-are-now-available-on-digitalocean-19865.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/coming-soon-gpu-droplets-nvidia-b300s>)

Author: Waverly Swinton

Published: 2025-12-15T17:51:51Z

Content type: release

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Digital Ocean](<https://devfeed.tech/topics/digital-ocean.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [virtual machines](<https://devfeed.tech/topics/virtual-machines.md>), [High-Performance Computing](<https://devfeed.tech/topics/high-performance-computing.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [data analytics](<https://devfeed.tech/topics/data-analytics.md>), [Multi-GPU](<https://devfeed.tech/topics/multi-gpu.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [compute](<https://devfeed.tech/tags/compute.md>), [digitalocean](<https://devfeed.tech/tags/digitalocean.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [high-performance-computing](<https://devfeed.tech/tags/high-performance-computing.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [multi-gpu](<https://devfeed.tech/tags/multi-gpu.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [product-updates](<https://devfeed.tech/tags/product-updates.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

DigitalOcean announces GPU Droplets accelerated by NVIDIA HGX B300, describing the platform's intended benefits for AI training, inference, generative AI, data analytics, and high-performance computing workloads.

### Source excerpt

AI continues to evolve at an unprecedented pace, with new models and demanding workloads pushing the boundaries of what's possible. From complex large language models (LLMs) to intricate scientific simulations, developers and businesses need access to the most powerful and efficient computing infrastructure. At DigitalOcean, we're committed to providing the cutting-edge tools you need to build, deploy, and scale your AI initiatives with simplicity and affordability. That's why we're excited to announce that GPU Droplets accelerated by NVIDIA HGX™ B300 are coming soon to DigitalOcean, marking a significant upgrade to our GPU offerings. Why NVIDIA HGX™ B300? The NVIDIA Blackwell Ultra accelerated computing platform represents a leap forward in AI reasoning. Designed for both training and inference, the NVIDIA HGX B300 offers substantial improvements in computational power, memory bandwidth, and energy efficiency compared to previous generations. The NVIDIA Blackwell architecture at the heart of the HGX B300 is not just about raw power; it's also about efficiency and innovation. With 1.5X more dense Tensor Core FLOPS, enhanced attention performance, and significantly expanded memory, the HGX B300 is optimized for the most demanding AI workloads including generative AI, data analytics, and high-performance computing (HPC). Featuring 7X more AI compute than NVIDIA Hopper platforms, 2.1TB of HBM3e memory, and high-performance networking integration with NVIDIA ConnectX-8 SuperNICs, Blackwell Ultra delivers breakthrough performance on the most complex workloads from agentic systems and reasoning, to real-time video generation. For AI-native enterprises running large reasoning models and long-context workloads, this enables: -Reduced model offloading and improved time-to-first-token -Higher sustained throughput under concurrency -More efficient multi-GPU scaling -Improved tokens-per-second per dollar Unlike GPU capacity providers, DigitalOcean integrates inference-optimized

## An AI Engineer's Guide To Choosing GPUs

DevFeed: [An AI Engineer's Guide To Choosing GPUs](<https://devfeed.tech/articles/an-ai-engineer-s-guide-to-choosing-gpus-35011.md>)

Original publisher: [Read original article](<https://read.theaimerge.com/p/an-ai-engineers-guide-to-choosing>)

Author: Alex Razvant

Published: 2025-12-07T14:02:40Z

Content type: tutorial

Language: en

Sources: [Neural Bits](<https://devfeed.tech/sources/neural-bits.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>), [Hopper](<https://devfeed.tech/topics/hopper.md>), [lora](<https://devfeed.tech/topics/lora.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Kernel](<https://devfeed.tech/topics/kernel.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-engineer](<https://devfeed.tech/tags/ai-engineer.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [hopper](<https://devfeed.tech/tags/hopper.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [ml](<https://devfeed.tech/tags/ml.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [pcie](<https://devfeed.tech/tags/pcie.md>), [software](<https://devfeed.tech/tags/software.md>), [technical](<https://devfeed.tech/tags/technical.md>)

### AI overview

A technical guide to choosing NVIDIA GPUs for AI workloads. It explains how GPU microarchitecture, memory subsystems, form factors, and interconnects affect capabilities, scaling, training, and inference, and compares consumer and data-center GPUs.

### Source excerpt

A deep dive on technical Hardware and Software details of NVIDIA GPUs for AI Workloads.

## Introducing Mistral 3

DevFeed: [Introducing Mistral 3](<https://devfeed.tech/articles/introducing-mistral-3-7040.md>)

Original publisher: [Read original article](<https://mistral.ai/news/mistral-3/>)

Published: 2025-12-02T16:00:00Z

Content type: release

Language: en

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

Topics: [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [models](<https://devfeed.tech/tags/models.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Mistral announces Mistral 3, a family of open models including compact dense models and Mistral Large 3, a sparse mixture-of-experts model with 41B active and 675B total parameters. The models are released under Apache 2.0, with compressed formats and optimized checkpoints intended to improve accessibility, customization, and deployment across developer and enterprise environments.

### Source excerpt

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