> ## Documentation Index > Fetch the complete documentation index at: https://docs.zylon.ai/llms.txt > Use this file to discover all available pages before exploring further. # External Grafana Dashboard This page is for teams that use their own Grafana instance. Zylon provides a reference dashboard for Triton and vLLM platform metrics, but importing it is a separate Grafana task. It is not required to enable observability in Zylon. Use it when you want an external Grafana dashboard for: * service health * throughput and failures * latency analysis * scheduler and GPU bottlenecks ## What you need first Before this dashboard is useful, you need: * platform metrics enabled in Zylon * a Prometheus-compatible metrics backend with Zylon metrics in it * a Grafana instance with a Prometheus datasource connected to that backend See [Platform Metrics](/en/operator-manual/configuration/observability/platform-metrics) and [Metrics Destinations](/en/operator-manual/configuration/observability/destinations). ## Import the dashboard Download [grafana-dashboard.json](https://raw.githubusercontent.com/zylon-ai/zylon-docs/main/snippets/grafana-dashboard.json) and import it in your Grafana instance through **Dashboards → New → Import**. For the Grafana import flow, see the [Grafana import dashboards documentation](https://grafana.com/docs/grafana/latest/visualizations/dashboards/build-dashboards/import-dashboards/). ## What the dashboard shows The dashboard is built from the metrics exposed on the Triton `/metrics` endpoint: * **Triton Inference Server** metrics such as request counts, latency, queue depth, and GPU health * **vLLM** metrics such as scheduler state, KV cache use, token throughput, and latency histograms ## Dashboard filters | Variable | Purpose | | --------------- | ----------------------------------------- | | **Datasource** | Prometheus datasource to query | | **Environment** | Deployment or company identifier | | **Model** | Model served by Triton | | **GPU** | `gpu_uuid` filter for GPU-specific panels | ## Reading the dashboard Follow this order when investigating an issue: | Section | What it helps you answer | | -------------------- | ----------------------------------------------------------- | | Overview | Is the service healthy right now? | | Throughput & Errors | How much traffic is it handling, and are requests failing? | | Latency | Where is time being spent? | | Capacity & Scheduler | Is the bottleneck queueing, KV cache pressure, or batching? | | Workload Analysis | What kind of requests are clients sending? | | GPU Health | Is the GPU saturated or memory constrained? | | Host Resources | Is the node itself under pressure? | ## Panels by section ### Overview Quick health indicators for success rate, requests per second, concurrent requests, and queue depth. Overview and Throughput & Errors sections ### Throughput & Errors Request rate, failure rate, failure reasons, batching behaviour, and queue depth over time. Throughput & Errors panels Failure breakdown by reason, inference count vs execution count, and pending request queue depth ### Latency End-to-end latency, phase breakdown, TTFT, TPOT, and request latency percentiles. Avg end-to-end latency and latency waterfall Avg queue, compute, and I/O overhead; Triton and vLLM TTFT percentiles Time per output token, end-to-end latency, prefill and decode time, Triton summary quantiles ### Capacity & Scheduler Scheduler state, queue time, KV cache utilisation, preemptions, and batch size. Capacity & Scheduler panels ### Workload Analysis Token throughput, prompt length, generation length, and prefix-cache behaviour. Token throughput, avg tokens per request, and prompt/generation length distributions Max generation tokens, max tokens per request percentiles, and prefix cache hit rate ### GPU Health GPU utilisation, memory pressure, power draw, and energy consumption. GPU utilization, memory, power, and energy consumption panels ### Host Resources CPU, RAM, and disk availability from `node_exporter`. CPU usage, RAM usage, and disk availability panels