Google Cloud TPUs made available to Hugging Face users
Hugging Face has announced the availability of Google Cloud TPUs for its Inference Endpoints and Spaces products. This integration allows Hugging Face users to deploy and run models on TPU hardware directly through the Hugging Face platform. The move expands the hardware options available to developers and researchers working with large models on Hugging Face infrastructure.
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Hugging Face on PyTorch / XLA TPUs
This Hugging Face blog post covers the integration of Hugging Face Transformers with PyTorch/XLA for training on Google TPUs. It describes how users can leverage TPU hardware through the XLA compiler backend to accelerate transformer model training. The post serves as a technical guide for the ecosystem connecting Hugging Face's model library with Google's TPU infrastructure.
Hugging Face and Google Partner for Open AI Collaboration
Hugging Face and Google have announced a partnership focused on open AI collaboration, expanding access to Hugging Face models and tools on Google Cloud Platform. The deal deepens integration between Hugging Face's model hub and Google's cloud infrastructure, enabling easier deployment of open-source models via GCP services. This follows a pattern of major cloud providers forming strategic alliances with leading open-source AI platforms.
Training a Language Model with Hugging Face Transformers Using TensorFlow and TPUs
This Hugging Face blog post provides a technical walkthrough for training a language model using TensorFlow and Google TPUs via the Transformers library. It covers the practical setup, data pipeline, and training configuration required to leverage TPU hardware with the TF ecosystem. The post serves as a tutorial bridging Hugging Face tooling with TPU-based infrastructure.
Hugging Face and Google Cloud Announce New Partnership
Hugging Face has announced a new partnership with Google Cloud, framed around building an open AI future. The blog post outlines collaboration between the two organizations, though the body content is not provided. This partnership likely involves deeper integration of Hugging Face's open-weights model hub and tooling with Google Cloud's infrastructure and services.
Bringing Serverless GPU Inference to Hugging Face Users via Cloudflare Workers AI
Hugging Face and Cloudflare have partnered to bring serverless GPU inference to Hugging Face users through Cloudflare Workers AI. The integration allows developers to run Hugging Face models on Cloudflare's global edge network without managing GPU infrastructure. This represents an expansion of serverless inference options for the Hugging Face ecosystem, lowering the barrier to deploying ML models at scale.
Hugging Face Launches Inference Providers on the Hub
Hugging Face has introduced Inference Providers on the Hub, a feature that allows users to run models hosted on the Hub through third-party inference providers directly from the platform. This integration consolidates access to multiple inference backends under a unified interface, reducing friction for developers who want to deploy or test models at scale. The announcement positions Hugging Face as a marketplace layer connecting model authors with inference infrastructure providers.
Easily Train Models with H100 GPUs on NVIDIA DGX Cloud
Hugging Face announced integration with NVIDIA DGX Cloud, enabling users to train models on H100 GPU clusters directly through the Hugging Face platform. The partnership simplifies access to high-end training infrastructure without requiring users to manage cloud provisioning themselves. This represents a continued push to lower the barrier to large-scale model training for the broader ML community.
Hugging Face and Graphcore Partner for IPU-Optimized Transformers
Hugging Face and Graphcore announced a partnership to optimize Transformer models for Graphcore's Intelligence Processing Unit (IPU) hardware. The collaboration aims to make IPU-accelerated inference and training accessible through the Hugging Face ecosystem. This represents an early effort to broaden AI hardware options beyond GPU-dominated infrastructure.