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.
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Hugging Face and NVIDIA Launch Training Cluster as a Service
Hugging Face and NVIDIA are announcing a joint 'Training Cluster as a Service' offering, providing managed GPU cluster access for AI model training. The collaboration aims to lower the barrier for organizations to access large-scale training infrastructure without managing hardware directly. This represents a strategic partnership between a major AI platform and a leading GPU manufacturer to address enterprise training infrastructure needs.
Serverless Inference with Hugging Face and NVIDIA NIM
Hugging Face and NVIDIA have partnered to offer serverless inference via NVIDIA NIM microservices on DGX Cloud infrastructure. The integration allows developers to run optimized model inference without managing GPU infrastructure, combining Hugging Face's model hub with NVIDIA's inference optimization stack. This represents an expansion of the existing Hugging Face–NVIDIA partnership into managed inference 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.
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.
Hugging Face and AMD Partner to Accelerate Models on CPU and GPU Platforms
Hugging Face and AMD announced a partnership aimed at optimizing and accelerating state-of-the-art AI models across AMD's CPU and GPU hardware platforms. The collaboration targets improved performance for models hosted and distributed through Hugging Face's ecosystem. This represents a strategic move to broaden hardware support beyond NVIDIA-dominated infrastructure in the AI/ML deployment landscape.
Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training
Habana Labs and Hugging Face announced a partnership to accelerate transformer model training on Habana's Gaudi AI processors. The collaboration aims to integrate Hugging Face's Transformers library with Habana's hardware, offering an alternative to GPU-based training infrastructure. This represents an early effort to diversify the AI training hardware ecosystem beyond NVIDIA dominance.
Deploy Hugging Face Models Easily with Amazon SageMaker
Hugging Face and Amazon SageMaker announced an integration enabling streamlined deployment of Hugging Face models via SageMaker's managed infrastructure. The partnership provides dedicated Hugging Face Deep Learning Containers on AWS, simplifying the path from model hub to production inference. This represents an early milestone in the enterprise deployment pattern of hosted model hubs integrating with cloud ML platforms.
Accelerating over 130,000 Hugging Face Models with ONNX Runtime
Hugging Face and Microsoft have integrated ONNX Runtime (ORT) to accelerate inference for over 130,000 models on the Hugging Face Hub. The integration enables optimized deployment across CPU and GPU hardware without requiring users to manually export or configure ONNX models. This represents a significant expansion of ORT's reach within the open-weights model ecosystem, lowering the barrier to production-grade inference optimization.