Kinetica Utilizes GPU Power for Faster Access to Real-Time Data for AI Applications


Key Takeaways:

– Kinetica is engaging with GPU-based database systems to boost retrieval augmented generation (RAG) capabilities.
– The company announces the ability to serve vector embeddings five times faster than other databases, as per the VectorDBBench benchmark.
– Rapid advancements help clients scan their data, including real-time data, more efficiently.
– The system offers the capability for users to interface with their entire corpus of relational enterprise data, streamlining operations.
– Kinetica harnesses the powerful computational capabilities of GPUs, bringing more speed and efficiency to the RAG process.

Kinetica’s GPU-Based Database Revolution

Best known for crafting a GPU-powered database to deliver rapid SQL queries and visualizations primarily for US government and military clients, Kinetica has set its sights on generating AI applications. The company made significant strides at Nvidia’s GTC show, highlighting how it is geared up for the emerging wave of generative AI applications, particularly those employing retrieval augmented generation (RAG) techniques.

Unlocking RAG Capabilities with GPU Power

Today, businesses are in a race to harness the potential of large language models (LLMs) with their proprietary data. Kinetica is forging a path by transforming its database into a vector vault that can store and dispatch vector embeddings to LLMs, significantly improving the data it delivers to the LLM. It leverages RAG, where applicable data is inserted directly into the context window before moving forward to the LLM for execution. This ensures the LLM response embodies greater personalization and context. Coupled with prompt engineering, RAG has emerged as an efficient way to boost GenAI returns.

Accelerating Data Access with Nvidia’s Blackwell GPU

The VRAM surge in Nvidia’s Blackwell GPU allows Kinetica to saturate the processor with data, catalyzing the process. According to their announcement at the show, Kinetica’s upgraded system can serve vector embeddings five times faster than its competitors. They attribute this achievement to their effective use of Nvidia’s RAPIDS RAFT technology.

Benefitting Clients with Quick and Real-Time Data Scanning

Their GPU-based speed advantage facilitates Kinetica’s customers to examine more of their data, including the real-time data that is newly logged in the database, without requiring extensive additional work. This essentially gift wraps users with the ability to interact with their comprehensive body of relational enterprise data, without any preplanning. Additionally, Kinetica can connect to other databases, function as a generative federated query engine, and carry out traditional data vectorization that customers input into Kinetica.

GPU, The Heart of Kinetica’s Offering

For Kinetica, the beating heart of their operations comes back to one thing: the GPU. Without the powerful computational capability of the GPU, they would just be one more in the pool of RAG offerings. The speed that comes from this powerful engine allows Kinetica to help customers leverage all their relational enterprise data within their LLM interaction. As shared by Nima Negahban, co-founder and CEO of Kinetica, the company provides all necessary orchestration for an easy end-user experience.

With such initiatives, Kinetica sets a higher bar for delivering fast access to real-time data. By harnessing the power of GPUs and making the most out of RAG, the company is equipping its users with a technology that optimizes and accelerates AI operations.

Jonathan Browne
Jonathan Browne
Jonathan Browne is the CEO and Founder of Livy.AI

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