Amazon.com’s cloud unit has announced a partnership with artificial intelligence startup Hugging Face to streamline the process of running AI models on Amazon’s custom computing chips. Hugging Face, valued at $4.5 billion, is a popular platform for AI researchers and developers to share AI software, with support from major companies like Amazon, Google, and Nvidia.
Developers often use Hugging Face to access and test open source AI models like Llama 3 from Meta Platforms. After modifying these models, developers typically want to integrate them into their own software applications. Through their partnership, Amazon and Hugging Face aim to enable developers to leverage these AI models on Amazon Web Services’ custom chip, Inferentia2.
Efficiency is a key focus for Hugging Face, as they strive to make AI models accessible to a larger audience and ensure their optimal performance. Amazon, on the other hand, is looking to attract more AI developers to utilize its cloud services for AI applications. While Nvidia is the leader in training AI models, AWS emphasizes that its chips are cost-effective for running inference tasks on the trained models.
Matt Wood, who leads AI products at AWS, highlighted the advantage of Inferentia2 in processing inference tasks efficiently, as these tasks are performed frequently compared to model training. The collaboration between Amazon and Hugging Face aims to facilitate the deployment of AI models on AWS infrastructure, providing developers with a seamless and cost-effective solution for running AI-powered applications.
Overall, the partnership between Amazon and Hugging Face signifies a step towards democratizing access to AI tools and making AI deployment more accessible and efficient for developers. The integration of Hugging Face’s AI models with AWS’s custom chips enhances the capabilities of running AI applications on the cloud, catering to the growing demand for AI technologies across industries. The collaboration exemplifies the ongoing innovation in the AI ecosystem and showcases the potential for advancements in AI deployment strategies.
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