Every week, I organize 400 to 700 links into roughly 60 categories as part of my ongoing effort to learn about AI. This is my personal notebook, which I enjoy sharing with friends… a hobby and a labor of love, rather than a commercial publication or product.
If you arrived here through a search or shared link, this page collects the links I found for NVIDIA for the week ending September 4, 2026.
As part of my learning process, I like to automate the category covers. It gives me a chance to learn Python and APIs.
This week’s cover prompt was written using Claude Opus 4.7, and the image was generated using Nano Banana 2.1.
Category Cover Image Prompt: A psychedelic 1960s Fillmore-style screen-printed gig poster of a giant illustrated graphics card as the headliner, its dual fans morphing into hypnotic op-art spirals that bloom into swirling magenta, acid orange, and deep teal waves, with lime green circuit-trace ribbons trailing like cables into autumn leaves and a crescent harvest moon in the ornate border, bold hand-lettered warped psychedelic title text reading NVIDIA arcing across the top, flat ink layers with slight registration offset on cream paper stock, vertical poster composition.
This Week in NVIDIA News
Summary by Claude Sonnet 5.5 based on this week’s links:
- Hugging Face acquisition: NVIDIA announced it will acquire Hugging Face for about $12.9 billion. NVIDIA said it will preserve the platform's openness and neutrality, and Michael Dell said NVIDIA committed to keeping it open on any silicon. Reaction from Jensen Huang, Satya Nadella and others was upbeat, and Hugging Face's Margaret Mitchell said the move is grounded in the company's openness values.
- Compute spending: Figure is partnering with Nscale to deploy up to 100,000 NVIDIA Vera Rubin GPUs, committing $3.5 billion initially and planning to scale beyond $6 billion. Separately, NVIDIA claims Vera Rubin NVL72 delivers up to 30x better throughput per megawatt than GB300 NVL72 on SemiAnalysis's AgentX workload.
- Open models and local AI: NVIDIA kept pushing its own open models, including the compact Nemotron 3.5 Lightning for always-on agents, and SafeMind, a CrowdStrike security model family built on Nemotron. A Jetson tutorial also shows how to fine-tune models with Unsloth and run them locally with llama.cpp.
This summary was generated by Claude Sonnet 5.5 to help you explore the links below. Rest assured, I select, organize, and check the links by hand in Google Sheets, and write the introduction and personal commentary in The Main Newsletters myself each week as a labor of love.
This week's links related to NVIDIA
NVIDIA and @CrowdStrike are advancing agentic cyber defense with SafeMind, a new family of security models and harnesses built on NVIDIA Nemotron. Introduced at Fal.Con, SafeMind is customized with CrowdStrike threat data to support triage and detection generation for cyber
https://x.com/nvidia/status/2094906811774013521
AI is reshaping the media and entertainment industry. At #IBC2026, NVIDIA is powering 30+ partner showcases across agentic creativity, sports intelligence and content authenticity. Joining us in Amsterdam? Explore live demos and expert sessions. Following from home? Discover
https://x.com/nvidia/status/2095202620528857459
Good old days at OpenAI in 2016: an agent stares at screen pixels, moves a mouse, and books a flight on United. We called it World of Bits, inside OpenAI Universe. 10 yrs later, Astra is reincarnated in the same universe. Even the naming is astronomically correct 😆 Universe was
https://x.com/DrJimFan/status/2095696882844463382
@DrJimFan @theworldlabs Indeed Jim, a camera conditioned world model with spatial context has so much horizontal usage including real2sim for robotics!
https://x.com/drfeifei/status/2094910083444707551
@theworldlabs Amazing work! Great step towards real2sim for robotics!
https://x.com/DrJimFan/status/2094905169460736291
Exciting day for NVIDIA and @huggingface. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI. Thank you
https://x.com/JensenHuang/status/2095482647355244762
This is good for everyone building and open source. Congrats @JensenHuang and @ClementDelangue
https://x.com/adcock_brett/status/2095603232458567917
Today, Figure is partnering with Nscale to deploy up to 100,000 GPUs on the NVIDIA Vera Rubin Platform We’re committing $3.5 billion initially, with plans to scale beyond $6 billion To ship a robot into every home, we need a massive amount of compute
https://x.com/adcock_brett/status/2095499534940185022
Training general-purpose humanoids will need unprecedented compute capacity, and Figure is stacking the chips. Figure, with cloud partner Nscale, is deploying up to 100,000 NVIDIA Vera Rubin GPUs. It’s committing $3.5 billion initially, with plans to scale to $6+ billion.
https://x.com/TheHumanoidHub/status/2095554390933549068
Super happy to share our intention to join forces with NVIDIA in a $12,930,300,000 acquisition 💛💚 10 years after starting Hugging Face, open-source AI is at an inflection point. Thanks to the community, we’ve shown that it can be a complement, and even an alternative, to
https://x.com/ClementDelangue/status/2095482998674112733
NVIDIA is buying Hugging Face for $12.9 billion. What does that mean for open-source AI? In 2012, Linus Torvalds called NVIDIA the worst company the Linux community had ever dealt with – and gave it the finger. Now NVIDIA maintains open Linux kernel modules, has released
https://x.com/TheTuringPost/status/2095552419807756793
NVIDIA to Acquire Hugging Face | NVIDIA Blog
https://blogs.nvidia.com/blog/nvidia-to-acquire-hugging-face/
Open models are essential to expanding access to AI and accelerating innovation around the world. We’re excited to help @huggingface scale its platform and community while preserving the openness, neutrality, and choice that have made it a trusted home for AI builders. 🤗💚
https://x.com/nvidia/status/2095518531693806017
Open models are for everyone and how AI reaches everyone. 18M developers. 3M models. 200K companies building on @huggingface. @Dell has been partnered with Hugging Face for years. Now with @nvidia’s backing and committing to keep it open on any silicon and anywhere, is a big
https://x.com/MichaelDell/status/2095528112662409503
Why is $12.9303B the most symbolic price tag ever? Look at the Unicode code point for the Hugging Face emoji: 🤗 = U+1F917 Convert 1F917 from hex to decimal: 129,303 The deal priced at the emoji! But it also is meanigful for @nvidia . Look at the letters of ticker NVDA mapped
https://x.com/TheTuringPost/status/2095556997655801926
Nvidia is now a humanoid robot designer. That’s the real takeaway from this news. Nvidia is acquiring Hugging Face for $12.93B. Hugging Face is the largest open-model and dataset hub, the “GitHub of AI,” with 3M+ models and 18M+ developers, robotics included. But here’s the
https://x.com/TheHumanoidHub/status/2095543869245108681
Nvidia in Talks to Invest Around $2.5 Billion in Murati’s Thinking Machines Lab … The Information
https://www.theinformation.com/articles/thinking-machines-lab-talks-raise-billions-roughly-40-billion-valuation
Nvidia’s AI advantage is moving beyond the GPU | TechCrunch
https://techcrunch.com/2026/08/29/nvidias-ai-advantage-is-moving-beyond-the-gpu/
Looking forward to this ecosystem with open models continuing to flourish and grow with NVIDIA and Hugging Face, and the continued partnership. Congrats @JensenHuang and @ClementDelangue!
https://x.com/satyanadella/status/2095587182039969861
⚡ Fine-tune and quantize AI models directly on NVIDIA Jetson. Our new Jetson AI Lab tutorial shows how to use Unsloth and memory-efficient QLoRA to customize models, export quantized GGUF files, and run them locally with llama.cpp. Follow hands-on examples for: 🔹 NVIDIA
https://x.com/NVIDIARobotics/status/2094480283135316182
Nvidia RTX Spark ‘Superchip’: The First AI PCs Are Here | WIRED
https://www.wired.com/story/nvidia-rtx-spark-laptops-first-look/
Personal AI Router for Local Inference | NVIDIA PAIR
https://www.nvidia.com/en-us/ai-on-rtx/personal-ai-router/
Are you measuring AI infrastructure performance on how agents actually run? @SemiAnalysis_ built AgentX to do just that. Vera Rubin NVL72 performance measured by NVIDIA on the SemiAnalysis AgentX workload shows up to 30x better throughput per megawatt than GB300 NVL72.
https://x.com/nvidia/status/2093722690049695780
Lightning fast to customize. Lightning fast to run. NVIDIA Nemotron 3.5 Lightning is a compact, customizable open model built to help always-on agents complete specialized tasks faster. Kari Briski joins @MTSlive to explain how Lightning helps always-on agents work faster.
https://x.com/nvidia/status/2093399189690691994
Jensen made a few calls to the partners from OpenAI, Grok, and Anthropic to celebrate the acquisition of Hugging Face 🙃 That’s why Gemini is not down
https://x.com/TheTuringPost/status/2095537216709869572
Open development helps AI move faster. When we can learn from, build on and improve each other’s work, we can make better technology together. Hear @ctnzr on the MAD Podcast with @mattturck
https://x.com/nvidia/status/2093457336161419676
Quick ethics take: A big part of why I joined @huggingface 5 years ago was the commitment to *critical* AI values: Openness, transparency, shared access. And we scaled this across the globe. Today, we’ve agreed to join NVIDIA… grounded on those values. I’ll share more later. =)
https://x.com/mmitchell_ai/status/2095536141810504101
The untold reason Jensen bought Hugging Face: Microduck is just too cute.
https://x.com/TheHumanoidHub/status/2095614659416232034
Marin 535B-A23B is 13% through training. This hero run would not be possible without the generous support of the Jen-Hsun and Lori Huang Foundation, which provided the funding for the compute (Coreweave). Thanks @JensenHuang for supporting open models!
https://x.com/percyliang/status/2095255747487740401
nvidia/DeepSeek-V4-Pro-0813-NVFP4 · Hugging Face
https://huggingface.co/nvidia/DeepSeek-V4-Pro-0813-NVFP4
This is what open-source intelligence is for. Built on vLLM-Omni and @haoailab’s FastH3, with @NVIDIAAI hardware support – all public. Real-time generation makes interactive video possible; an open baseline makes it improvable for everyone. Take it and go faster! 🚀
https://x.com/MiniMax_AI/status/2094926136333787512
Across Europe, AI communities have been moving the field forward for years. This October, Berlin becomes a meeting point for the people building what comes next. 🤝 Join researchers, developers, engineers, and industry leaders at #NVIDIAGTC Berlin, October 20-22. Register
https://x.com/nvidia/status/2095187846462054455
@TencentHunyuan’s Hy4-preview runs in vLLM from day 0, verified on NVIDIA GPUs. 🎉 – 770B total, 49B active, 256 routed experts plus one shared – 1M context, but each query attends to just 2048 tokens – Only 21 of the 78 layers compute their own sparse index, the other 57 reuse
https://x.com/vllm_project/status/2093248073057357905
What used to take years now takes a week. @TAMU’s VISION, an NVIDIA DGX SuperPOD and the most powerful academic supercomputer on the June 2026 TOP500, is giving researchers across seven institutions the compute to tackle challenges at unprecedented scale and speed.
https://x.com/nvidia/status/2093484389682270429
NVIDIA LPU supports 3 types of disaggregated inferencing: 1. Rubin Prefill + LPU Decode for the fastest interactivity 2. Rubin Prefill + Rubin Decode Attention + LPU Decode FFN for the middle of the curve 3. Rubin Prefill + Rubin Decode Verification + LPU Drafter for the
https://x.com/SemiAnalysis_/status/2094470943619842286
We’re excited to expand our work with @MediaTek. 🤝 AI factories will be built around differentiated compute … but custom silicon needs a path from chip design to production-scale systems. With MediaTek adopting NVIDIA NVLink Fusion, customers can focus on what makes their XPUs
https://x.com/nvidia/status/2094440470814609812
Join @baseten, @nvidia Dynamo, and SGLang on 9/10 for an evening dedicated to the infrastructure behind reinforcement learning post-training! We will present the latest on SGLang & Miles. Miles is an RL training framework using SGLang as the rollout inference engine to make RL
https://x.com/sgl_project/status/2095200888197722439
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