Nvidia invests $5 billion into Intel to jointly develop PC and data center chips

NVIDIA and Microsoft Reinvent Windows PCs for the Age of Personal AI

RTX Spark — a 1-Petaflop Superchip, the Full CUDA and RTX Ecosystem, and Windows-Native Agents — a New Beginning for Personal Computers News Summary: NVIDIA RTX Spark powers the world’s first Windows PCs purpose-built for personal agents, featuring 1 petaflop of AI performance, industry-leading power efficiency, full-stack NVIDIA AI and graphics technology, and up to 128GB of unified memory. NVIDIA and Microsoft collaborate to deliver a native Windows experience for personal agents, including new security primitives and NVIDIA OpenShell to run agents securely on primary devices. RTX Spark lets creators, AI developers and gamers render ultralarge 90GB+ 3D scenes, edit 12K 4:2:2 video, generate 4K AI videos, run 120B-parameter LLMs with up to 1 million tokens context using agents locally, and play AAA games at 1440p and over 100 frames per second. Adobe is rearchitecting Photoshop and Premiere from the ground up for RTX Spark to deliver 2x faster AI and graphics performance. RTX

GPU Pricing — Live Platform Rates | Vast.ai

Live GPU pricing on Vast.ai. Prices set by supply and demand across 40+ data centers. On-demand, interruptible, or reserved — find the right GPU at the right price.

NVIDIA Partners With Microsoft on Unified Stack for Agentic AI Deployment, From Windows Devices to Cloud to Local

At Microsoft Build, NVIDIA founder and CEO Jensen Huang joined Microsoft chairman and CEO Satya Nadella's keynote via livestream from Taipei to discuss the expanded partnership.

"AI" centralization

So it seems like NVIDIA has agreed to buy Hugging Face. NVIDIA is the company building most of the chips used in “AI” data centers and Hugging face runs probably the biggest repository for data sets and open weight “AI” models in the world (Hugging Face also offers inference but not at relevant rates and […]

NVIDIA DGX Station for Windows Puts a Trillion-Parameter AI Supercomputer on Every Enterprise Desk

News Summary: NVIDIA announces DGX Station for Windows — the world’s most powerful deskside AI supercomputer for developing and running agents on Windows — built on the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, coming in Q4 this year. DGX Station brings frontier AI agents to Windows — enabling enterprise developers, researchers, engineers, designers and data scientists to build and deploy AI across the workflows and applications their business runs on. DGX Station will support NVIDIA OpenShell on Windows, built on new Windows security and containment primitives. TAIPEI, Taiwan, June 01, 2026 (GLOBE NEWSWIRE) - NVIDIA GTC Taipei - NVIDIA today announced NVIDIA DGX Station™ for Windows , the world’s most powerful deskside AI supercomputer designed to build, run and connect always-on AI agents to Windows applications and workflows, capable of running frontier AI models of up to 1 trillion parameters locally. Historically, heavy-duty enterprise AI workloads —

TPUs vs. GPUs and why Google is positioned to win AI race in the long term | Hacker News

To quote The Next Platform: "An Ironwood cluster linked with Google’s absolutely unique optical circuit switch interconnect can bring to bear 9,216 Ironwood TPUs with a combined 1.77 PB of HBM memory... This makes a rackscale Nvidia system based on 144 “Blackwell” GPU chiplets with an aggregate of 20.7 TB of HBM memory look like a joke."

Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical Blog

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…

0xSero on Twitter / X

I told y’all this is the move. Heterogenous hardware is the way forward. Large cheap pools of mixed memory + specialized accelerators (Nvidia GPUs, DGX Spark, Cerebras wafers) The next year will be dominated by solutions that split the stack. - 3000$ for a used Mac Studio… https://t.co/zMlScSnJ0X— 0xSero (@0xSero) April 1, 2026

NVIDIA NIM for Developers

NVIDIA NIM is a set of accelerated inference microservices that allow organizations to run AI models on NVIDIA GPUs anywhere—in the cloud, data center, workstations, and PCs.

Rohan Paul on Twitter / X

Google is trying to win AI by making compute cheap, not by beating Nvidia on raw speed.Nvidia sells GPUs to clouds with a big 70%+ margin that sits on top of manufacturing and R&D cost and raises cloud prices.Google builds TPUs for itself at near manufacturing cost, adds no… https://t.co/aSgWRf0HY7 pic.twitter.com/T3Fzc6czwg— Rohan Paul (@rohanpaul_ai) November 25, 2025

AI Chip Architectures

A look at AI Chip Architectures. NVIDIA, AMD, TPUs, Trainium, Groq, Cerebras.

Chris Lattner on Twitter / X

Please don’t tell anyone: we aren’t just open sourcing all the models. We are doing the unspeakable: open sourcing all the gpu kernels too. Making them run on multivendor consumer hardware, and opening the door to folks who can beat our work.Plz keep it quiet, ok? 😉— Chris Lattner (@clattner_llvm) March 24, 2026

NVIDIA Levels Up Local AI Agents Across RTX PCs and DGX Spark

Announced at GTC Taipei at COMPUTEX, NVIDIA OpenShell brings secure agents to Windows with 2x inference performance on llama.cpp — plus, Adobe rebuilds its apps with performance and memory enhancements, and Blender adds NVIDIA DLSS 4.5 Ray Reconstruction for NVIDIA RTX Spark.

SF Compute: Commoditizing Compute to solve the GPU Bubble forever

Selling GPUs to avoid bankruptcy, empowering researchers with short term clusters, and why CoreWeave is maybe a real estate business

NVIDIA Shatters MoE AI Performance Records With a Massive 10x Leap on GB200 'Blackwell' NVL72 Servers, Fueled by Co-Design Breakthroughs

Scaling performance on MoE AI models is one of the industry constraints, but it appears that NVIDIA has managed to make a breakthrough.