Nvidia agreed to buy Hugging Face for just over $12.93 billion, after acquisition talks that reportedly began a few weeks earlier. The deal is valued at $12.9B. Nvidia says Hugging Face will keep supporting multi-chip and multi-cloud AI projects, while Nvidia gains data on which datasets, models, and architectures are being used.
Nvidia agreed to acquire Hugging Face in a $12.9 billion deal and says the platform will stay open despite Nvidia control. The announcement includes a projected closing date in the first half of 2027. As part of the acquisition, Nvidia promises Hugging Face will remain compute-agnostic and continue supporting multiple clouds and accelerators so developers can deploy open models without requiring Nvidia compute.
Nvidia agreed to buy AI platform Hugging Face in a bid to expand from AI chips into software. The deal is valued at about $12.9bn (£9.5bn). Nvidia will pay about $11.9bn to Hugging Face investors and offer up to $1bn in stock incentives, while saying Hugging Face will remain open and not force users onto Nvidia chips or services.
Nvidia confirmed its plan to buy Hugging Face, the open-source AI model repository, for $12.93bn. The acquisition is expected to close in the first half of 2027. Nvidia says it will scale the Hugging Face platform, strengthen its infrastructure, and expand access to AI for developers and institutions.
Zvi (Don't Worry About the Vase)·1 month ago·
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The article surveys a backlog of postmortem coverage after the Hugging Face hack and pivots to a new wave of upcoming or recently mentioned language-model releases. A concrete detail is Claude Code’s weekly limits increase starting September 14, when standard weekly limits rise by 25% for Pro, Max, Team, and seat-based Enterprise plans. As a result, the author plans minimal attention for several claimed step-forward models while focusing coverage next on Fable 5.1 and OpenAI’s Astra, alongside smaller notes on interpretability and usage limits.
Nvidia agreed to buy AI model platform Hugging Face for $13 billion. The deal price is $13 billion. The acquisition shifts Hugging Face’s ownership under Nvidia, aiming to accelerate the spread of open-weight models.
NeoMME released a multilingual multimodal encoder family and a NeoMME-Retriever fine-tune for visual document retrieval that uses a single bidirectional Transformer without separate pretrained vision or language towers.
Nvidia confirmed it will acquire Hugging Face after weeks of rumors. Nvidia will pay $12.93 billion for the platform. Nvidia said Hugging Face will keep supporting open-source/open-weight models while expanding developer access, and Nvidia compute will not be required to use or deploy through Hugging Face.
Nvidia agreed to acquire Hugging Face to expand the infrastructure behind the open-model platform. Nvidia will pay $12,930,300,000. Hugging Face will remain open, keep its team and brand, and NVIDIA compute will not be required to build on or deploy through it, though the deal still needs formal completion and may face antitrust scrutiny.
NVIDIA agreed to acquire Hugging Face. The deal price is $12,930,300,000. Hugging Face will remain an open platform supporting multi-cloud and open-weight models, with no required NVIDIA compute to use or deploy on it.
Nvidia agreed to buy Hugging Face, bringing the open-source model and dataset hosting platform under the ownership of the AI chipmaker. The deal price is $12.93 billion. Hugging Face will shift from independent ownership to being part of Nvidia, changing its platform ownership and likely its investment priorities for AI tooling and hosting infrastructure.
Coding agents using separate sessions across machines lose prior context because their reasoning and trace logs aren’t automatically usable in later runs. On the handoff-vs-recall benchmark, recall was 8x cheaper than a written handoff on one task and 4x on the other. The funes tool adds a durable, locally computed memory layer that indexes prior agent turns and lets agents retrieve the original passages with provenance across sessions, agents, and machines.
A language model training pipeline was built to generate p5.brush JavaScript sketches that paint watercolor-style images, using an open RL environment and open reward pool tied to aesthetic preference judgments. The post reports an HF command that trains with 110 steps and 240 episodes per run. As a result, three GRPO training runs with different reward-model weightings produced comparable watercolor outputs and all the RL environment, pool, training scripts, and trained models were published to run end-to-end on Hugging Face.
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