Volumez DIaaS for AI/ML

The rapid adoption of artificial intelligence and machine learning systems in every industry has created an unprecedented scaling challenge as businesses convert data from knowledge into intelligence. These advanced use cases can be solved now with Volumez’ Data Infrastructure as a Service (DIaaS) for AI/ML where storage, compute, and networking resources are abstracted from their physical locations and intelligently orchestrated to optimize the cloud data infrastructure across the AI/ML data pipeline. Volumez makes AI/ML data services highly performant, scalable, and efficient across any cloud, keeping GPU utilization near 100%, reducing training times, increasing model accuracy, improving data scientist productivity with parallel jobs, and automating data infrastructure tasks in complex MLOps environments.

Apply for the Volumez DIaaS for AI Training Early Access program here

Volumez breaks through the AI/ML performance barrier with unprecedented results in the MLPerf® Storage 1.0 AI/ML Training Benchmark

We’re honored Volumez has been named the Undisputed Leader in the first MLCommons® MLPerf Storage Benchmark for AI/ML Training workloads. Volumez’s impressive results demonstrate superiority on cloud-native data infrastructure.

Results verified by MLCommons Association.¹

Performance

Train larger models in less time with the Volumez DIaaS for AI/ML platform. Volumez delivers 1.1TB/sec throughput, 92.2% GPU utilization, and 9.9M IOPS of guaranteed performance to each GPU server, enabling ML engineers and MLOps to deliver better, faster business insights.

Scalability

Eliminate the shared storage I/O bottleneck that limits the scale of today’s AI/ML training cycles. Volumez controller-less architecture independently connects each GPU directly to raw NVMe media, eliminating noisy neighbors and the scalability limits of storage controllers, metadata servers, and cluster locks.

Efficiency

Lower GPU hardware costs while increasing training cycle performance. Volumez eliminates idle time imposed on GPU servers due to I/O bottlenecks, improving time to insights and reducing total cost of training.