Systalyze Open-Sources Utilyze: A More Accurate GPU Monitoring Tool to Combat Misleading Utilization Metrics
Key Takeaways
- ▸Standard GPU utilization metrics (nvidia-smi, nvtop, AWS CloudWatch, Google Cloud Monitoring, Azure Monitor) are misleading—they only report whether a GPU is running, not how hard it's actually working
- ▸Systalyze's Utilyze provides accurate GPU utilization measurement by sampling hardware performance counters and calculating compute and memory throughput relative to theoretical limits
- ▸Accurate GPU measurement is critical infrastructure as GPU scarcity drives costs: H100 rental pricing rose ~40% from October 2025 to March 2026, making optimization essential
Summary
Systalyze has open-sourced Utilyze (Apache 2.0), a free GPU monitoring tool designed to address a widespread measurement problem across the AI industry. The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, and major cloud providers (AWS, Google Cloud, Azure) is fundamentally misleading—it only indicates whether a GPU is running any kernel at all, not how efficiently it's actually working. In production deployments, workloads have shown real compute throughput as low as 1–10% while dashboards reported 100% utilization.
Utilyze solves this by sampling hardware performance counters to measure compute and memory throughput relative to theoretical hardware limits, while also estimating an attainable utilization ceiling for specific workloads. This addresses a critical gap in capacity planning and optimization decisions: teams unknowingly purchasing additional GPUs while existing hardware sits underutilized, wasting billions in unnecessary spending and energy consumption. The tool runs alongside AI workloads with negligible overhead and has revealed "orders-of-magnitude performance headroom" in production systems that standard tools declared fully saturated.
- The tool is free, open-source (Apache 2.0), production-ready, and available on GitHub—addressing an industry-wide problem that drives unnecessary hardware purchases and energy waste
Editorial Opinion
This release highlights a fundamental gap in how the AI industry measures success. When standard monitoring tools across NVIDIA, major cloud providers, and popular ML platforms all report the same misleading metric, the problem becomes systemic—teams cannot optimize what they cannot measure. Utilyze's emphasis on measuring actual compute throughput rather than kernel activity is not just a technical improvement; it's a necessary correction that could unlock significant cost savings and energy efficiency gains across AI deployments.
