Nvidia Agrees to Acquire Hugging Face for 2.9 Billion

Nvidia has reached an agreement to acquire open-source AI platform Hugging Face for $12.9 billion, according to reporting from The Information. The acquisition marks the largest software and developer platform purchase in Nvidia's history, securing direct control over the primary distribution hub for open-weight artificial intelligence models. Hugging Face, founded in 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf, operates the standard repository for open-source machine learning we

2 min
Nvidia Agrees to Acquire Hugging Face for 2.9 Billion

Nvidia has reached an agreement to acquire open-source AI platform Hugging Face for $12.9 billion, according to reporting from The Information. The acquisition marks the largest software and developer platform purchase in Nvidia's history, securing direct control over the primary distribution hub for open-weight artificial intelligence models.

Hugging Face, founded in 2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf, operates the standard repository for open-source machine learning weights, datasets, and spaces. The agreed transaction values the company at approximately 80 times its current annualized revenue run-rate of $150 million, up from $100 million recorded earlier this year.

Valuation History and Transaction Terms

The $12.9 billion buyout represents a steep premium over Hugging Face's previous private funding rounds:

  • August 2023: Raised $235 million in Series D funding at a $4.5 billion valuation, backed by Salesforce Ventures, Google's GV, IBM Ventures, and Nvidia.
  • Early 2026: Declined a $500 million direct investment proposal from Nvidia that would have valued the business at $7 billion.
  • August 2026: Finalized acquisition agreement at $12.9 billion following buyout discussions reported by Business Insider at valuations exceeding $13 billion.

Hugging Face leadership previously noted that the company had approached operational breakeven as developer demand for hosted model endpoints and enterprise spaces expanded over the past year.

Mid-century modernist diagram illustrating open-weight model distribution networks and GPU compute nodes

Strategic Push to Anchor Open Weights on GPU Infrastructure

The acquisition comes as leading closed-model frontier labs increasingly seek to reduce reliance on Nvidia hardware:

  • Proprietary ASIC Development: OpenAI is developing in-house inference processors with Broadcom while securing compute agreements with Cerebras and AMD. Anthropic continues development on proprietary silicon, and Google has expanded internal deployments of its custom Tensor Processing Units (TPUs).
  • Open-Source Counterweight: Open-weight model families such as Qwen, DeepSeek, and GLM continue to gain developer adoption across enterprise environments. By owning the central distribution and inference gateway for these architectures, Nvidia solidifies developer reliance on its CUDA software stack and standard GPU clusters.
  • Inference and Cloud Routing: Hugging Face offers serverless inference and dedicated container deployment. The platform allows Nvidia to route compute workloads directly to its cloud hardware infrastructure, serving as an integrated developer hub across enterprise and independent deployments.

The deal also follows broader consolidation across AI developer tooling and routing layers, including Stripe's recent $7 billion acquisition of AI routing startup OpenRouter.

Sources

Written by

More to read

  • Anthropic Agrees to 5 Billion Cloud Deal with Nscale for 460MW of Vera Rubin Compute

    Anthropic has finalized a six-year, $45 billion cloud computing agreement with AI infrastructure provider Nscale. Under the terms of the deal, Anthropic will secure approximately 460 megawatts of dedicated computing capacity at Nscale's Monarch data center development in West Virginia, scheduled to come online in late 2027. The deployment will be powered by Nvidia's upcoming Vera Rubin architecture, providing compute bandwidth for next-generation foundation model training and enterprise inferen

    1 min
  • OpenAI Details Custom Inference Chip 'Jalapeño' at Hot Chips, Targeting 700W Efficiency Against Nvidia Blackwell

    OpenAI has revealed architectural specifications and benchmark data for its first in-house artificial intelligence accelerator, code-named Jalapeño. Presented by hardware lead Richard Ho at the Hot Chips conference at Stanford University, the application-specific integrated circuit (ASIC) is engineered specifically for large language model inference rather than model training. Developed over an 18-month partnership with Broadcom and manufactured by TSMC, the chip targets large-scale token gener

    1 min
  • Low-Rank Adaptation (LoRA) and QLoRA: Mathematical Foundations, Intrinsic Rank Parameterization, NF4 Quantization, and Double Quantization Mechanics

    Low-Rank Adaptation (LoRA) and QLoRA: Mathematical Foundations, Intrinsic Rank Parameterization, NF4 Quantization, and Double Quantization Mechanics Parameter-efficient fine-tuning (PEFT) has become the standard operational methodology for adapting large language models to domain-specific tasks, downstream instruction following, and structured tool use. Full-parameter fine-tuning of frontier architectures requires updating and tracking optimizer states for tens or hundreds of billions of parame

    1 min