VAST Data unveils new AI cloud architecture & partnerships ahead of NVIDIA conference
AI data platform company, VAST Data, has unveiled new data centre architecture and partnerships ahead of NVIDIA’s GTC conference. This comes as a result of a collaboration with NVIDIA with an aim to amplify performance, service, zero-trust security, and efficiency in order to aid organisations in building and scaling AI services.
VAST’s new architecture presents a novel approach to AI cloud architecture by embedding storage and database processing services directly into AI servers. Furthermore, it offers linear data services designed to scale to hundreds of thousands of graphics processing units (GPUs).
Simultaneously, VAST has announced its partnership with server, AI, storage, IoT, and switch systems specialist Supermicro to deliver a comprehensive, AI system that eases the creation and expansion of large-scale AI deployments. This system is anticipated to offer service providers and enterprises multi-tenancy, zero-trust security, and scalability, which specifically targets organisations dealing with extensive volumes of proprietary data.
The new AI cloud architecture is constructed on NVIDIA BlueField-3 data processing unit (DPU) technology. The architecture aims to expedite and make efficient AI factories by “disaggregating the entirety of VAST’s operating system natively into AI computing machinery, transforming supercomputers into AI data engines,” as the press release elaborates. It further explains how NVIDIA BlueField DPUs can now be leveraged to realise the full potential of visions for disaggregated data centres.
Co-founder at VAST Data, Jeff Denworth, expressed his thoughts, “This new architecture is the perfect showcase to express the parallelism of the VAST Data Platform. With NVIDIA BlueField-3 DPUs, we can now realise the full potential of our vision for disaggregated data centres that we’ve been working toward since the company was founded.”
Rob Davis, Vice President of Storage Technology at NVIDIA, praised VAST’s architecture and expressed its potential benefits for enterprises and service providers alike. “With VAST’s operating system, next-generation accelerated computing solutions are paired with next-generation accelerated network infrastructure, enabling enterprises and service providers to benefit from simpler, more secure experiences with high-performance systems,” he said.
The new VAST architecture is being trialled and deployed firstly at CoreWeave, a top-tier specialised GPU cloud provider. VAST and CoreWeave have been collaborating since 2023 to build some of the world’s most scalable AI machinery.
CoreWeave’s Vice President of Engineering, Peter Salanki, commented on their partnership, “VAST’s revolutionary architecture is a game-changer for CoreWeave… Now, by natively incorporating storage and database services onto BlueField, we’re not just streamlining our infrastructure but we are also elevating the user experience for our customers by removing bottlenecks in the AI data computing pipeline.”
In parallel to this partnership, VAST has joined forces with Supermicro to deliver a full-stack, AI solution aimed at simplifying the creation and expansion of large-scale AI deployments for service providers and large, data-centric enterprises. This solution promises multi-tenancy, zero-trust security and scalability which is crucial for organisations dealing with large volumes of proprietary data volatile to change.
Renen Hallak, CEO and co-founder of VAST Data, expressed his anticipation for the collaboration with Supermicro, “With a revolutionary parallel architecture that is built with enterprise data management tools for unstructured and structured data, this joint solution is the ideal platform for the modern era of AI computing.”
The CEO at Supermicro, Charles Liang, voiced his excitement about the partnership, “Leveraging Supermicro’s NVIDIA-Certified Systems portfolio with VAST Data’s software platform combines the compute and data functions into a Supermicro platform that’s ideal for large-scale AI deployments, enabling significant deep learning performance.”