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.
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Hugging Face and FriendliAI Partner to Supercharge Model Deployment on the Hub
Hugging Face and FriendliAI have announced a partnership to integrate FriendliAI's inference infrastructure directly into the Hugging Face Hub. The collaboration aims to simplify and accelerate model deployment for developers accessing models through the Hub. This expands the ecosystem of inference providers available on Hugging Face's platform.
Hugging Face Adds Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita
Hugging Face has expanded its serverless inference provider ecosystem by integrating three new partners: Hyperbolic, Nebius AI Studio, and Novita. These providers offer API-based inference for models hosted on the Hugging Face Hub, increasing the options available to developers for deploying open-weights models without managing infrastructure. The expansion reflects growing competition in the inference-as-a-service market targeting open-source AI workloads.
Cohere Models Now Available via Hugging Face Inference Providers
Hugging Face has added Cohere as an inference provider on its platform, enabling users to access Cohere models directly through the Hugging Face Inference API. This integration expands the Inference Providers ecosystem, which allows developers to run models from multiple vendors through a unified interface. The announcement reflects continued consolidation of model serving infrastructure across major AI providers.
DeepInfra Added as Hugging Face Inference Provider
Hugging Face has added DeepInfra as an integrated inference provider on its platform. This expands the roster of third-party inference backends accessible directly through the Hugging Face ecosystem. The integration allows users to route model inference requests to DeepInfra's infrastructure via the standard Hugging Face Inference Providers interface.
Hugging Face Launches Inference for PRO Subscribers
Hugging Face introduced a dedicated inference tier for PRO subscribers, providing access to powerful models via API without rate limits typical of free tiers. The offering targets developers and researchers who need reliable, higher-throughput access to hosted models. This represents a monetization and infrastructure expansion move by Hugging Face to serve professional users.
Featherless AI Joins Hugging Face Inference Providers
Hugging Face has added Featherless AI as a new inference provider in its Inference Providers ecosystem. Featherless AI specializes in serverless inference for open-weight models, expanding the range of third-party compute options available through the Hugging Face platform. This integration allows developers to route model inference requests to Featherless AI directly via the Hugging Face API and model hub.
Public AI on Hugging Face Inference Providers
Hugging Face announces the integration of Public AI as a new inference provider on its platform. This expands the ecosystem of third-party inference backends available through Hugging Face's unified API. The move continues the pattern of Hugging Face aggregating multiple inference providers to give developers flexible deployment options.
Announcing New Hugging Face and KerasHub Integration
Hugging Face and KerasHub have announced a new integration enabling users to access Hugging Face models and datasets directly through the Keras ecosystem. This partnership bridges two major ML frameworks, allowing Keras users to leverage the Hugging Face Hub's model repository without leaving the Keras workflow. The integration is aimed at reducing friction for practitioners who prefer Keras-based training and inference pipelines.