> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.
# GPU Performance Monitoring
> Monitor NVIDIA and AMD GPUs with key metrics like usage, temperature, and power using OpenTelemetry for AI workloads
OpenLIT uses OpenTelemetry to help you monitor NVIDIA and AMD GPUs for AI applications. Track GPU metrics like utilization, temperature, memory usage, and power consumption during AI training and inference workloads.
## Choose your method
GPU monitoring can be implemented in two ways depending on your setup and requirements:
}
>
It is useful if you already have an AI application running on GPU that's instrumented with OpenLIT.
It extends your existing observability to include GPU metrics alongside your LLM traces.
}
>
It is useful for remote GPUs with only LLM models hosted, containerized deployments.
This approach allows you to get GPU metrics without modifying application code.
## Supported Parameters
### SDK Configuration Options
| Parameter | Type | Default | Description |
| ------------------------ | ------- | ------------------- | ------------------------------------------- |
| `collect_system_metrics` | boolean | `False` | Enable GPU and system metrics collection |
| `otlp_endpoint` | string | None | OpenTelemetry OTLP endpoint URL |
| `otlp_headers` | string | None | Authentication headers for OTLP endpoint |
| `service_name` | string | `"unknown_service"` | Name of your AI application |
| `environment` | string | None | Deployment environment (dev, staging, prod) |
### Environment Variables
| Variable | Description | Example |
| -------------------------------- | -------------------------- | ----------------------- |
| `OPENLIT_COLLECT_SYSTEM_METRICS` | Enable GPU monitoring | `true` |
| `OTEL_EXPORTER_OTLP_ENDPOINT` | OTLP endpoint URL | `http://127.0.0.1:4318` |
| `OTEL_SERVICE_NAME` | Service name for telemetry | `my-gpu-app` |
| `OTEL_DEPLOYMENT_ENVIRONMENT` | Deployment environment | `production` |
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Deployment options for scalable LLM monitoring infrastructure
60+ AI integrations with automatic instrumentation and performance tracking
Send telemetry to Datadog, Grafana, New Relic, and other observability stacks
Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.