NVIDIA와 Hugging Face의 결합, 개방형 모델·장기형 에이전트·로봇 컴퓨트

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 closed-source APIs. But for it to happen at larger scale, it needs more compute, more support, more collaboration and more visibility. That’s why we went to talk to Jensen, who offered to do exactly that with us. In addition to doubling down on NVIDIA’s massive contributions to open-source AI (I called them the “King of American open-source AI” earlier this year), they’ve committed to strongly supporting Hugging Face and our mission while keeping the platform open, independent and compute agnostic. The founders and the team are all staying to keep pushing this mission forward. Together, we think we can make open source the default way to build AI, with the goal of empowering 100 million AI builders to own their intelligence rather than rent it. Excited about the next 10 years! 🤗🤗🤗

Jensen Huang

@JensenHuang

23시간 전

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 @ClementDelangue for coming to me. NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗

Introducing K2 Horizon: a connected fleet of six foundation models ranging from 0.9 billion to 375 billion parameters. - Frontier performance: Across coding and agentic tasks, K2 Horizon delivers top-tier performance in every size class—with the 0.9B, 3.7B and 7B models setting new state of the art at their respective scales. - Radical openness: K2 Horizon represents the largest fully open-source model launch in AI history. The fully open code, training data and recipes are a significant step forward in transparency. Launch page: https:// ifm.ai/k2/ Tech blog: https:// ifm.ai/blog/k2 Hugging Face: https:// huggingface.co/collections/IF M/k2-horizon …

We’re proud to see MiniMax-M3 powering HUMAIN-M3. Built on the M3 foundation and further trained on more than 1 trillion Arabic tokens, HUMAIN-M3 brings frontier-level capabilities to Arabic, spanning diverse languages and regional dialects. This is a meaningful milestone for Arabic AI, and a great example of how open foundation models can be localized and built upon to advance AI ecosystems around the world.

Meet E-Commerce Bench, a new benchmark for long-horizon autonomous business operations. 🚀 Agents start with ¥100,000 to run online stores for 365 days, handling sourcing, negotiation, pricing, promotions, inventory and cash flow, in a market driven by real e-commerce data. What's inside: 👀 - Real economics: 6886 products, 576 suppliers (152 fraudsters), 600-minute workdays, storage fees, returns and reputation. - Long-horizon learning: almost no model learns to buy cheaper or improve its strategies over a full year of operation. - Seven-axis evaluation: beyond year-end assets, we score six more dimensions, revealing that no single model dominates across the board. Learn more about E-Commerce Bench: 👇 - Blog: https:// qwen.ai/blog?id=e-comm erce-bench … - Paper: https:// arxiv.org/abs/2608.30730 - Project:  https:// ecbench.github.io - Code:  https:// github.com/QwenLM/E-Comme rceBench …

🗑️ AI agents need better garbage collection. Xiaohongshu researchers built Self-GC, using a planner LLM to decide which context tokens to keep, fold, or prune. In tests, it retained necessary details 84.85 percent of the time compared to just 54.55 percent for standard methods. Master agent memory management: https:// hubs.la/Q04wy8ML0 #DeepLearningAI #AIAgents #LLMs

Ok Fable 5.1 is insane. People can't stop building games + entire worlds with it. 10 wild examples:

10. Minecraft meets Red Dead Redemption meshup

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

The 100,000 GPUs targeted for initial deployment starting the second half of 2027 in Barstow, Texas We wrote a write-up:

Discover how FireDucks can speed up pandas workloads with lazy execution, compiler optimization, and multithreaded processing, delivering up to 20x faster DataFrame performance in our benchmark.