GPU Observability: Get Deeper Insights into Your Droplets and DOKS Clusters

GPU Observability: Get Deeper Insights into Your Droplets and DOKS Clusters Why GPU Observability Matters What’s Included: New Metric Categories Zero Setup, No Extra Cost Benefits of GPU Droplets with DigitalOcean About the author Try DigitalOcean for free Related Articles Image and audio models from fal now available on DigitalOcean Announcing GPU Droplets accelerated by NVIDIA HGX H100 in the EU Announcing cost-efficient storage with Network file storage, cold storage, and usage-based backups By Waverly Swinton Updated: November 12, 2025 2 min read We’re introducing a new set of basic observability metrics for all GPU Droplets and DOKS clusters , giving you a powerful, simple way to monitor and optimize your AI workloads. When running large-scale training, inference, and complex data processing—cluster performance and stability are paramount. Our new observability features are designed to give you the visibility you need to ensure effective utilization of your resources and quickly debug any performance bottlenecks. Get real-time, individual metrics from your NVIDIA and AMD GPUs and their network interfaces on critical factors like utilization, temperature, power consumption, and more—all directly within the DigitalOcean Insights UI, and with zero setup required. We’ve grouped the new metrics into five intuitive categories to provide a comprehensive view of your GPU and DOKS cluster health and performance: Utilization: Understand how busy your GPU cores and memory are. This includes key metrics like GPU Occupancy and Memory Utilization, allowing you to optimize your setup for peak performance live. Utilization: Understand how busy your GPU cores and memory are. This includes key metrics like GPU Occupancy and Memory Utilization, allowing you to optimize your setup for peak performance live. Temperature: Monitor thermal conditions to prevent overheating and ensure stable operation under heavy load. Power: Track power consumption, which is essential for understanding GPU performance and efficiency. Throttle: Identify if your GPU is limiting its performance due to thermal, power, or voltage constraints. This is crucial for debugging sudden performance degradations.