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Virtana’s full-stack observability platform integrates natively with NVIDIA GPU platforms to offer in-depth insights into AI environments by continuously collecting telemetry

May 21, 2025 //  by Finnovate

Virtana announced the launch of Virtana AI Factory Observability (AIFO), a powerful new capability that extends Virtana’s full-stack observability platform to the unique demands of AI infrastructure. With deep, real-time insights into everything from GPU utilization and training bottlenecks to power consumption and cost drivers, AIFO enables enterprises to turn complex, compute-intensive AI environments into scalable, efficient, and accountable operations. This launch strengthens Virtana’s position as the industry’s broadest and deepest observability platform, spanning AI, infrastructure, and applications across hybrid and multi-cloud environments.  Virtana’s AI Factory Observability (AIFO) helps enterprises treat AI infrastructure with the same level of visibility, discipline, and accountability as traditional IT. As an official NVIDIA partner, Virtana integrates natively with NVIDIA GPU platforms to deliver in-depth telemetry, including memory utilization, thermal behavior, and power metrics, providing precise, vendor-validated insight into the most performance-critical components of the AI Factory. This deep integration delivers accurate, actionable intelligence at enterprise scale.  Virtana AI Factory Observability (AIFO) is purpose-built to meet the demands of AI operations. It continuously collects telemetry across GPUs, CPUs, memory, network, and storage and then correlates that data with training and inference pipelines to provide clear and actionable insights. Core capabilities include: GPU Performance Monitoring; Distributed Training Visibility; Infrastructure-to-AI Mapping; Power and Cost Analytics; Root Cause Analysis. AIFO is already delivering measurable results in production AI environments across multiple industries. Operational outcomes include: 40% reduction in idle GPU time, improving resource utilization and reducing infrastructure costs; 60% faster mean time to resolution (MTTR) for AI-related incidents; 50% decrease in false alerts, reducing operational noise and accelerating response; 15% improvement in power efficiency, supporting sustainability goals.

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