Data is doubling every two years, driven by human and machine-generated data, such as the Internet of Things (IoT). The analysis of patData is doubling every two years, driven by human and machine-generated data, such as the Internet of Things (IoT). The analysis of patterns and structure in this data and its transformation into actionable insights is driven by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) workloads.
IDC predicts that in the next three years over half the businesses will use AI, ML, and DL workloads. This has given rise to the NVIDIA DGX-1 GPU-based supercomputer to accelerate these workloads. Vexata supports the DGX-1 and delivers ultra-low latency, massive ingest bandwidth, and heavy mixed random and sequential read/write I/O operations. Using direct attached storage (DAS) or all-flash arrays (AFAs) limits the usefulness of the DGX-1 by restraining performance and requiring complex data provisioning, mobility, and growth. Vexata addresses these issues with VelocityAI, which combines the NVIDIA DGX-1 and the Vexata VX-100FS Scalable NVMe File Array.
Learn about VelocityAI and how we built it to overcome these machine learning challenges. This webinar is targeted to those that want to leverage GPU for data analytics. Our expert will cover:
- Dealing with large training and inferencing data-sets by leveraging NVMe media
- Using the VelocityAI to eliminate movement between data pipeline stages
- Dealing with data security, protection, and efficiency concerns
Learn how you can leverage VelocityAI to deliver transformative performance with breakthrough economics at scale for your AI, ML, and DL applications.