Infinidat announced its Retrieval-Augmented Generation (RAG) workflow deployment architecture to enable enterprises to fully leverage generative AI (GenAI).
This dramatically improves the accuracy and relevancy of AI models with up-to-date, private data from multiple company data sources, including unstructured data and structured data, such as databases, from existing Infinidat platforms.
With Infinidat’s RAG architecture, enterprises utilize Infinidat’s existing InfiniBox and InfiniBox SSA enterprise storage systems as the basis to optimize the output of AI models, without the need to purchase any specialized equipment.
Infinidat also provides the flexibility of using RAG in a hybrid multi-cloud environment, with InfuzeOS™ Cloud Edition, making the storage infrastructure a strategic asset for unlocking the business value of GenAI applications for enterprises.
“Infinidat will play a critical role in RAG deployments, leveraging data on InfiniBox enterprise storage solutions, which are perfectly suited for retrieval-based AI workloads,” said Eric Herzog, CMO at Infinidat.
Herzog added, “Vector databases that are central to obtaining the information to increase the accuracy of GenAI models run extremely well in Infinidat’s storage environment. Our customers can deploy RAG on their existing storage infrastructure, taking advantage of the InfiniBox system’s high performance, industry-leading low latency, and unique Neural Cache technology, enabling delivery of rapid and highly accurate responses for GenAI workloads.”
RAG augments AI models using relevant and private data retrieved from an enterprise’s vector databases. Vector databases are offered by a number of vendors, such as Oracle, PostgreSQL, MongoDB and DataStax Enterprise.
These are used during the AI inference process that follows AI training. As part of a GenAI framework, RAG enables enterprises to auto-generate more accurate, more informed and more reliable responses to user queries.
It enables AI learning models, such as a Large Language Model (LLM) or a Small Language Model (SLM), to reference information and knowledge that is beyond the data on which it was trained.
It not only customizes general models with a business’ most updated information, but it also eliminates the need for continually re-training AI models, which are resource intensive.
“Infinidat is positioning itself the right way as an enabler of RAG inferencing in the GenAI space,” said Marc Staimer, President of Dragon Slayer Consulting. “Retrieval-augmented generation is a high value proposition area for an enterprise storage solution provider that delivers high levels of performance, 100% guaranteed availability, scalability, and cyber resilience that readily apply to LLM RAG inferencing. With RAG inferencing being part of almost every enterprise AI project, the opportunity for Infinidat to expand its impact in the enterprise market with its highly targeted RAG reference architecture is significant.”
To read the FULL Press Release as well as the AI Workload Solution Brief and blog, go to the following webpage: https://www.infinidat.com/en/news/press-releases/infinidat-introduces-retrieval-augmented-generation-rag-workflow-deployment
