gpu docs update

install-and-configure/advanced-configuration/gpu.md

@@ -20,6 +20,10 @@ In cases where you have already installed DCGM Exporter in your cluster, Kubecos
   - `app.kubernetes.io/name`
 - The value of one of these labels must contain the string `dcgm-exporter`.
 
+
+**In order for Kubecost to provide gpuUsageAverage, gpuRequestAverage and gpuCostIdle, the DCGM exporter must be running locally on the cluster with GPU nodes.**
+
+
 ## Install DCGM Exporter
 
 DCGM Exporter is an implementation of NVIDIA [Data Center GPU Manager (DCGM)](https://developer.nvidia.com/dcgm) for Kubernetes which exports metrics in [Prometheus](https://prometheus.io/) format. DCGM Exporter allows for running the DCGM software under Kubernetes on nodes which contain NVIDIA devices and takes care of the task of making DCGM metrics available to external tools such as Kubecost.

using-kubecost/navigating-the-kubecost-ui/efficiency-dashboard.md

@@ -58,6 +58,8 @@ Once we’ve identified clusters of interest, you can click on the workload idle
 **This is where you can analyze your workload idle costs.** Workload idle is broken down by resource (CPU, RAM, and GPU) for each workload (namespace, controller, etc.).
 It is important to remember that workload idle is defined as the cost of resources which are requested but not used. There may be cases where a given workload’s usage exceeds its request – for these cases, the workload idle costs will be $0.00.
 
+**Important Note: Kubecost must be measuring some amount of GPU usage before it will show GPU Efficiency features.**
+
 **How can I reduce my workload idle costs?**
 Kubecost is full of insights and automation to help you reduce your wasted workload idle cost!
 Specifically, the [right-size container requests](/using-kubecost/navigating-the-kubecost-ui/savings/container-request-right-sizing-recommendations.md) page can help drive significant reductions in workload idle.