Observability

NIM provides Prometheus metrics indicating request statistics. These metrics can be used to create dashboards with the Grafana dashboard. By default, these metrics are available at http://localhost:8000/v1/metrics.

curl -X 'GET' 'http://0.0.0.0:8000/v1/metrics'

The following table describes the available metrics.

Category	Metric	Metric Name	Description	Granularity	Frequency
KV Cache Count	GPU Cache Usage	gpu_cache_usage_perc	GPU KV-cache usage. 1 means 100 percent usage	Per model	Per iteration
	Running Count	num_requests_running	Number of requests currently running on GPU	Per model	Per iteration
	Waiting Count	num_requests_waiting	Number of requests waiting to be processed	Per model	Per iteration
	Max Request Count	num_request_max	Max number of concurrently running requests	Per model	Per iteration
	Total Prompt Token Count	prompt_tokens_total	Number of prefill tokens processed	Per model	Per iteration
	Total Generation Token Count	generation_tokens_total	Number of generation tokens processed	Per model	Per iteration
Latency	Time to First Token	time_to_first_token_seconds	Histogram of time to first token in seconds	Per model	Per request
	Time per Output Token	time_per_output_token_seconds	Histogram of time per output token in seconds	Per model	Per request
	End to End Request Latency	e2e_request_latency_seconds	Histogram of end to end request latency in seconds	Per model	Per request
	Vision Encoder Latency	vision_encoder_latency_seconds	Histogram of vision encoder latency in seconds	Per model	Per request
Count	Prompt Token Count	request_prompt_tokens	Histogram of number of prefill tokens processed	Per model	Per request
	Generation Token Count	request_generation_tokens	Histogram of number of generation tokens processed	Per model	Per request
	Finished Request Count	request_finish_total	Number of finished requests, with label indicating finish reason	Per model	Per request
	Successful Request Count	request_success_total	Number of successful requests	Per model	Per request
	Successful Request Count	request_failure_total	Number of failed requests	Per model	Per request
	Image Count	request_image_count	Histogram of the number of images per request	Per model	Per request
Image	Image size	image_size_pixels	Histogram of image sizes in pixels	Per model	Per request

Prometheus

To install Prometheus for scraping metrics from NIM, download the latest Prometheus version appropriate for your system.

wget https://github.com/prometheus/prometheus/releases/download/v2.52.0/prometheus-2.52.0.linux-amd64.tar.gz
tar -xvzf prometheus-2.52.0.linux-amd64.tar.gz
cd prometheus-2.52.0.linux-amd64/

Edit the Prometheus configuration file prometheus.yml to scrape from the NIM endpoint. Make sure the targets field point to localhost:8000.

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: "prometheus"

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
      - targets: ["localhost:8000"]

Run the Prometheus server

./prometheus --config.file=./prometheus.yml

Open a browser and point it to http://localhost:9090/targets?search= to check that the NIM target was detected by the Prometheus server. You can also click on the NIM target url link to explore the generated metrics.

Grafana

We can use Grafana for dashboarding NIM metrics. Install the latest Grafana version appropriate for your system.

wget https://dl.grafana.com/oss/release/grafana-11.0.0.linux-amd64.tar.gz
tar -zxvf grafana-11.0.0.linux-amd64.tar.gz
cd grafana-v11.0.0/

Run the Grafana server

./bin/grafana-server

To access the Grafana dashboard, point your browser to http://localhost:3000. You will need to log in using the defaults.

username: admin
password: admin

The first step is to configure the source from which Grafana can scrape metrics. Click on the “Data Source” button, select Prometheus, and specify the Prometheus URL localhost:9090. After saving the configuration, you should see a success message.

Now you are ready to create a dashboard with metrics from NIM. This Grafana tutorial provides step-by-step instructions on building a dashboard from scratch. Alternatively, you can jump start using this example dashboard.

Vision metrics

Vision-related metrics (e.g., Vision Encoder Latency) are only available for the TRT-LLM backend (the vLLM backend only collects end-to-end metrics). Consider starting with this example dashboard instead if NIM runs TRT-LLM optimized engines.