Netweb Technologies Launches Tyrone ParallelStor Velox, a Unified Data Platform for AI and HPC
Netweb Technologies launched Tyrone ParallelStor Velox, a unified data platform with parallel file system capabilities. It addresses data bottlenecks in AI, HPC, and enterprise infrastructure by unifying data across various storage tiers and enabling high-speed access. The platform supports NVIDIA GPUDirect Storage for accelerated AI workloads.
The launch of a new, advanced data platform has the potential to enhance the company's competitive edge and revenue streams in the growing AI and HPC markets, impacting its future business.
The announcement details the launch of a new product aimed at solving critical challenges in AI and HPC infrastructure, which is a positive development for the company's technological offerings and market position.
Netweb Technologies India Limited has announced the launch of Tyrone ParallelStor Velox, a Unified Data Platform designed to address data bottlenecks in AI, HPC, and enterprise infrastructure. This platform features parallel file system capabilities and aims to ensure data can be accessed, moved, and processed at the speed required by modern compute demands.
Velox unifies data across flash, disk, tape, and cloud into a single global namespace, eliminating silos and enabling high-speed access. It supports advanced data path optimization technologies, including NVIDIA GPUDirect Storage, to accelerate AI data pipelines, leading to higher GPU utilization and faster model training.
The platform is designed for hybrid and distributed environments, offering a single unified data platform across on-prem and cloud, multi-protocol access (POSIX, NFS, SMB, S3/Swift, Hadoop) on the same dataset, and policy-driven data lifecycle management. It is built for data-intensive sectors like AI & HPC, Government & Public Sector, and BFSI, aligning with the need for sovereign AI infrastructure.
Swastik Chakraborty, VP at Netweb Technologies, stated, "With Tyrone ParallelStor Velox, we are enabling organizations to move beyond fragmented storage and adopt a unified data platform that can keep pace with AI, HPC, and enterprise workloads at scale."
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