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NVIDIA Unveils Groundbreaking NeMo Retriever Microservices for Multilingual Information Retrieval and Efficient Data Storage



NVIDIA has unveiled its latest innovation, NeMo Retriever microservices, designed to bridge linguistic and contextual divides in enterprise AI. This technology enables organizations to process more information at once and fit large knowledge bases on a single server, achieving accurate, scalable, and high-impact results.

  • NVIDIA's NeMo Retriever microservices aim to revolutionize multilingual information retrieval and efficient data storage for generative AI applications.
  • The technology bridges linguistic and contextual divides, enabling organizations to process more information at once and fit large knowledge bases on a single server.
  • NeMo Retriever supports efficient and accurate text retrieval across multiple languages and cross-lingual datasets.
  • The partnership between DataStax and Wikimedia has successfully implemented NeMo Retriever, reducing processing time from 30 days to under three days.
  • NVIDIA partners are adopting NeMo Retriever microservices to connect custom models to diverse and large data sources globally.


  • NVIDIA has recently unveiled its latest innovation, NeMo Retriever microservices, designed to revolutionize multilingual information retrieval and efficient data storage for generative AI applications. This groundbreaking technology promises to bridge the linguistic and contextual divides in enterprise AI, enabling organizations to process more information at once and fit large knowledge bases on a single server.

    The introduction of NeMo Retriever microservices marks a significant milestone in NVIDIA's ongoing efforts to empower enterprises with cutting-edge AI solutions. These microservices are built on top of the company's robust NIM platform and leverage advanced techniques such as retrieval-augmented generation (RAG) to access richer, more relevant information.

    According to the context data provided, NeMo Retriever microservices support efficient and accurate text retrieval across multiple languages and cross-lingual datasets. This feature is particularly important for enterprise AI applications that require seamless connectivity with diverse and large data sources. By deploying these microservices, organizations can unlock the full potential of their data, achieving accurate, scalable, and high-impact results.

    One notable example of the successful adoption of NeMo Retriever microservices is the partnership between DataStax and Wikimedia. Together, they have implemented NeMo Retriever to vector-embed the content of Wikipedia, serving billions of users. This groundbreaking project has resulted in a significant reduction in processing time, with the ability to serve users in under three days, previously taking 30 days.

    Furthermore, leading NVIDIA partners such as Cohesity, Cloudera, Nutanix, SAP, VAST Data, and WEKA are already adopting NeMo Retriever microservices to help organizations across industries securely connect custom models to diverse and large data sources. These partnerships demonstrate the widespread potential of NeMo Retriever microservices and their ability to drive business impact globally.

    In addition, NVIDIA's platform and consulting partners play a critical role in ensuring enterprises can efficiently adopt and integrate generative AI capabilities, such as NeMo Retriever microservices. These partners help align AI solutions to an organization's unique needs and resources, making generative AI more accessible and effective.

    The NeMo Retriever microservices are now available on the NVIDIA API catalog, or a no-cost, 90-day NVIDIA AI Enterprise developer license, allowing developers to access these groundbreaking technologies and build efficient information retrieval systems. With its advanced capabilities and seamless integration with existing NIM platform, NeMo Retriever microservices promise to revolutionize multilingual information retrieval and data storage for generative AI applications.



    Related Information:

  • https://blogs.nvidia.com/blog/nemo-retriever-nim/

  • https://developer.nvidia.com/nemo-retriever

  • https://blogs.nvidia.com/blog/nemo-retriever-microservices/


  • Published: Tue Dec 17 12:02:44 2024 by llama3.2 3B Q4_K_M











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