Horizontal Autoscaling of NVIDIA NIM Microservices on Kubernetes

NVIDIA NIM microservices are model inference containers that can be deployed on Kubernetes. In a production environment, it’s important to understand the…

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NVIDIA
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Juana Nakfour
•7 min read•advanced•
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GrafanaHelmHugging FaceJSONKubernetesMicroservicesPrometheus

Overview

This article discusses the horizontal autoscaling of NVIDIA NIM microservices on Kubernetes, focusing on how to set up Kubernetes Horizontal Pod Autoscaling (HPA) based on custom metrics like GPU cache utilization. It provides a step-by-step guide for deploying NVIDIA NIM for LLMs and monitoring performance using tools like Prometheus and Grafana.

What You'll Learn

1

How to set up Kubernetes Horizontal Pod Autoscaling for NVIDIA NIM microservices

2

Why monitoring GPU cache utilization is crucial for autoscaling

3

How to deploy NVIDIA NIM for LLMs using Helm

4

When to use Prometheus and Grafana for monitoring Kubernetes applications

Prerequisites & Requirements

  • Understanding of Kubernetes and microservices architecture
  • NVIDIA AI Enterprise license
  • Kubernetes cluster version 1.29 or later
  • Kubernetes CLI tool kubectl installed
  • HELM CLI installed
  • Admin access to the Kubernetes cluster

Key Questions Answered

How do you set up Horizontal Pod Autoscaling for NVIDIA NIM microservices?
To set up Horizontal Pod Autoscaling (HPA) for NVIDIA NIM microservices, you need to install the Kubernetes Metrics Server, Prometheus, and Grafana. Then, you deploy the NIM microservice and create an HPA resource that scales based on specific metrics, such as GPU cache utilization.
What prerequisites are needed for deploying NVIDIA NIM for LLMs?
You need an NVIDIA AI Enterprise license, a Kubernetes cluster version 1.29 or later, admin access to the cluster, and tools like kubectl and HELM installed. Understanding Kubernetes and microservices architecture is also essential.
What tools are used for monitoring NVIDIA NIM microservices?
Prometheus and Grafana are used for monitoring NVIDIA NIM microservices. Prometheus scrapes metrics from the microservices, while Grafana provides a dashboard for visualizing these metrics, including GPU cache utilization.
How does the HPA scale based on GPU cache utilization?
The HPA scales based on the 'gpu_cache_usage_perc' metric, which indicates the GPU cache utilization. You can set minimum and maximum replicas in the HPA configuration, allowing the service to automatically scale up or down based on the specified metric thresholds.

Key Statistics & Figures

KV cache percent utilization
9.40% to 40.9%
This metric increased with varying concurrency levels during traffic generation, indicating how effectively the cache is utilized under load.

Technologies & Tools

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Orchestration
Kubernetes
Used for deploying and managing NVIDIA NIM microservices.
Monitoring
Prometheus
Used for scraping metrics from the deployed microservices.
Monitoring
Grafana
Used for visualizing metrics and creating dashboards.
Package Management
Helm
Used for deploying applications and managing Kubernetes resources.

Key Actionable Insights

1
Implementing Horizontal Pod Autoscaling can significantly improve resource utilization in your Kubernetes cluster.
By scaling microservices based on actual usage metrics, such as GPU cache utilization, you can ensure that your applications remain responsive under varying loads without over-provisioning resources.
2
Utilizing Prometheus and Grafana for monitoring provides deep insights into your application's performance.
These tools allow you to visualize metrics in real-time, making it easier to identify bottlenecks and optimize resource allocation in your Kubernetes environment.
3
Deploying NIM for LLMs can enhance the performance of AI models in production environments.
By leveraging NVIDIA's optimized microservices, you can achieve better inference times and resource efficiency, which is crucial for handling large language models.

Common Pitfalls

1
Failing to correctly configure the Prometheus adapter can lead to missing metrics for HPA.
If the Prometheus adapter is not pointing to the correct Prometheus service endpoint, HPA will not receive the necessary metrics to make scaling decisions, which can hinder the application's performance.

Related Concepts

Kubernetes Horizontal Pod Autoscaling
Nvidia Nim For Llms
Prometheus And Grafana Monitoring
Custom Metrics For Autoscaling