In a significant move to bolster enterprise AI capabilities, Hugging Face and Google Cloud have announced an expanded partnership that aims to enhance the accessibility and deployment of open models. This collaboration seeks to empower businesses to easily build their AI solutions using Hugging Face’s resources on Google Cloud’s robust infrastructure.
Access to Open Models
Google Cloud clients have been leveraging open models from Hugging Face across several platforms. Within Vertex AI, users can access a variety of popular open models through Model Garden. For those requiring customized solutions, model libraries are available via GKE AI/ML or in pre-configured environments managed by Hugging Face. Furthermore, AI inference workloads are effectively managed on Cloud Run GPUs, promoting serverless deployments. These integrations provide businesses with multiple options for deploying and managing their AI solutions across Google Cloud’s diverse services.
Expanded Infrastructure
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Study Flags ‘Questionable’ Sources in Elon Musk’s Grokipedia, Risking MisinformationThe partnership will introduce a new Content Delivery Network (CDN) Gateway, specifically optimized for hosting Hugging Face models and datasets. This CDN Gateway, which utilizes Hugging Face’s Xet storage technology alongside Google Cloud’s networking infrastructure, is expected to enhance model download speeds and improve supply chain reliability. It will support various platforms, including Vertex AI, GKE, Cloud Run, and Compute Engine.
Remarkably, usage of Hugging Face by Google Cloud customers has surged tenfold over the past three years, resulting in tens of petabytes of model downloads each month. The new CDN Gateway is designed to accommodate this growth, helping businesses minimize time-to-first-token and improve model governance.
Hugging Face Tools
Additionally, Hugging Face’s Inference Endpoints will now benefit from Google Cloud’s infrastructure, providing access to more instance types and potential cost reductions. These enhancements will cater to the large community of AI developers utilizing the Hugging Face platform, facilitating seamless model deployment from Hugging Face directly to Google Cloud.
With Google Cloud’s seventh generation Tensor Processing Units (TPUs), Hugging Face is actively working to offer native support for these chips within its libraries. This initiative aims to simplify the process of adopting TPUs for executing open models on Google Cloud, ensuring that they are compatible with GPU setups in the Hugging Face environment.
Security Initiatives
On the security front, the two companies are enhancing protective measures for open models hosted by Hugging Face. They will incorporate Google’s advanced security technologies, including VirusTotal, Google Threat Intelligence, and Mandiant, to secure hosted models, datasets, and associated spaces. This focus on security is designed to strengthen the supply chain for developers and organizations utilizing the Hugging Face Hub.
Industry Outlook
“Google has made some of the most impactful contributions to open AI, from the OG transformer to the Gemma models. I believe in a future where all companies will build and customize their own AI. With this new strategic partnership, we’re making it easy to do on Google Cloud,” said Jeff Boudier, Product and Growth at Hugging Face.
Echoing this sentiment, Ryan J. Salva, Senior Director of Product Management at Google Cloud, stated, “Hugging Face has been the driving force enabling companies large and small all over the world to access, use, and customize now more than 2 million open models, and we’ve been proud to contribute over 1,000 of our models to the community. Together we will make Google Cloud the best place to build with open models.”
This expanded partnership between Hugging Face and Google Cloud not only underscores the growing importance of open AI models for enterprises but also highlights the collaborative efforts aimed at streamlining AI deployment and enhancing security measures in the evolving landscape of artificial intelligence.

















































