After 18 years in software engineering and extensive experience optimizing GPU infrastructure across enterprise environments, I’ve learned that effective observability transforms how we understand and optimize AI workloads. With only 7% of companies achieving over 85% GPU utilization during peak periods, the opportunity for improvement is massive.
Leading cross-functional teams, I’ve witnessed how inadequate monitoring strategies create invisible bottlenecks. In previous roles, I’ve discovered GPU clusters running at suboptimal efficiency during critical training periods, problems that went undetected for months. The root cause was consistently the same: Teams were tracking basic utilization metrics without understanding the deeper performance indicators.
Initially, our monitoring relied solely on in-band metrics like standard GPU utilization, thermal readings and power consumption, which gave us limited visibility into deeper inefficiencies. We quickly realized that thermal and power-related throttling were key contributors to performance degradation, but the in-band metrics cannot reliably detect underlying hardware issues like failing fans or GPUs hitting thermal or power limits silently. To address this, we introduced out-of-band (OOB) GPU telemetry, which allowed us to monitor hardware health independently of the operating system and workload layers.
Recent industry research shows that 74% of companies are dissatisfied with their current GPU scheduling and monitoring tools. This dissatisfaction stems from a fundamental misunderstanding: GPU utilization percentage alone tells you almost nothing about actual performance. Through experience, I’ve learned that you can achieve 100% GPU utilization by just reading/writing to memory while doing zero computations.
Through years of optimization work, I’ve identified the critical metrics that drive real performance improvements. GPU Streaming Multiprocessor (SM) Clock Speed reveals whether your GPUs are thermally throttling or power-limited. I’ve seen teams celebrate high utilization while missing that their GPUs were running at reduced clock speeds due to inadequate cooling.
Memory Bandwidth Utilization often becomes the hidden bottleneck. In optimization projects, I’ve discovered that improving memory access patterns can dramatically improve overall throughput, even when GPU utilization appears healthy. Power Consumption Patterns provide early warning signs. Monitoring power draw alongside temperature helps prevent the thermal throttling that silently destroys performance.
Model FLOPS Utilization (MFU) measures how effectively you’re using the GPU’s computational capacity. This metric, borrowed from HPC practices, has become essential for optimization efforts.
Teams have found success implementing observability using the Grafana LGTM stack (Loki, Grafana, Tempo, Mimir). This open-source combination provides the flexibility needed for comprehensive GPU monitoring while maintaining cost efficiency.
Initially stages of our setup, we used DataDog primarily for collecting metrics. However, as our observability requirements grew, with the addition of more detailed GPU monitoring, log ingestion and complex dashboards, we quickly encountered cost and scalability concerns with proprietary solutions like DataDog. We evaluated several alternative observability stacks and ultimately chose the LGTM stack because it better aligned with our needs for flexibility, cost efficiency and control. The LGTM stack enabled us to tailor dashboards, retain logs for longer periods and correlate traces and metrics effectively. This was particularly useful for GPU-intensive workloads where we needed to visualize thermal throttling, memory bandwidth usage, and power metrics over time. Another key factor was OpenTelemetry. Adopting the OpenTelemetry collector helped us unify telemetry data ingestion across services, allowing us to efficiently standardize log and metric collection from diverse sources into the LGTM stack.
With Grafana Mimir handling metrics at scale and Loki aggregating logs from thousands of training jobs, teams can build dashboards that correlate GPU performance with model training efficiency. The key is creating a unified view that connects infrastructure metrics with application-level insights.
Starting with automated baseline detection is crucial. Modern observability platforms using AI/ML techniques can identify performance anomalies that manual threshold-based monitoring would miss. This proactive approach helps catch memory leaks and inefficient kernel launches before they impact production workloads.
The real power of comprehensive observability emerges when you connect performance metrics to business outcomes. Research indicates that organizations can achieve a 35% reduction in costs through adaptive observability practices.
Dynamic workload scheduling based on real-time GPU metrics can significantly improve cluster utilization. This approach focuses on understanding when GPUs are truly productive versus merely busy, leading to substantial efficiency gains without requiring additional hardware investment.
Successful optimization strategies include implementing multi-instance GPU (MIG) partitioning based on workload profiling to maximize resource utilization, creating feedback loops between observability data and job schedulers to prevent resource waste and establishing workload-specific performance baselines to identify optimization opportunities quickly.
As AI workloads continue to grow in complexity, observability must evolve from reactive monitoring to proactive optimization. The organizations that master GPU observability today will have the competitive advantage tomorrow, not just in cost savings, but in the ability to iterate faster and deploy more sophisticated models.
The journey from basic monitoring to comprehensive observability requires commitment, but the rewards are substantial. Focus on metrics that reveal true performance bottlenecks, build integrated observability frameworks that connect infrastructure to application outcomes and always tie performance improvements to measurable business value.
For teams just starting with GPU observability, the goal should be to implement a Minimum Viable Observability (MVO) setup that is lightweight but delivers immediate value in detecting performance bottlenecks, inefficiencies, or system stress. Run NVIDIA DCGM exporter as a daemonset(in Kubernetes) or standalone on GPU nodes. It exposes metrics at the `/metrics` endpoint in Prometheus format. Use the Prometheus receiver in OpenTelemetry Collector to scrape the DCGM exporter. Observability backends can be Grafana, New Relic, Datadog, etc. Add alerts for unused GPU, overheating and power approaching limits.
