Today’s best AI agents rely on retrieval-augmented generation (RAG) to enable more accurate results. A RAG system facilitates the use of a knowledge base to…
Overview
This article discusses the implementation of horizontal autoscaling for Retrieval-Augmented Generation (RAG) components on Kubernetes, focusing on NVIDIA's microservices architecture. It provides insights into managing unpredictable workloads and maintaining performance standards through effective scaling strategies.
What You'll Learn
1
How to implement Horizontal Pod Autoscaling (HPA) for NVIDIA NIM microservices
2
Why understanding latency metrics is crucial for autoscaling
3
How to use Prometheus metrics for monitoring and scaling applications
Prerequisites & Requirements
- Understanding of Kubernetes and microservices architecture
- Kubernetes CLI (kubectl) and HELM CLI installed
- Admin access to the Kubernetes cluster
Key Questions Answered
How does a RAG system work?
A RAG system processes user queries by retrieving context from a vector database, reranking the results, and generating answers using a large language model (LLM). It involves ingestion, retrieval, and answer generation phases, with performance metrics like latency and throughput being critical for scaling.
What are the prerequisites for deploying NVIDIA RAG components?
To deploy NVIDIA RAG components, you need an NGC account for API keys, admin access to a Kubernetes cluster, and tools like kubectl and HELM installed. The cluster should also be set up with Metrics Server, Prometheus, and Grafana.
How can you autoscale the LLM NIM based on latency?
You can create a custom metric for the 90th percentile of time to first token (TTFT) and set up Horizontal Pod Autoscaler (HPA) to scale the LLM NIM when TTFT exceeds 2 seconds. This ensures responsiveness during high load.
What metrics are used for autoscaling NVIDIA NIM microservices?
Key metrics for autoscaling include concurrency (num_requests_running), GPU cache usage (gpu_cache_usage_perc), and latency (TTFT). These metrics help maintain performance and meet service level agreements during varying workloads.
Key Statistics & Figures
Maximum concurrent requests for customer service chatbot
300 concurrent requests
CR
Latency SLA for TTFT
TTFT < 2 seconds
This is the threshold for scaling the LLM NIM to ensure responsiveness.
GPU utilization for scaling
> 75%
This is the threshold for scaling the reranking and embedding NIM microservices.
Technologies & Tools
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Orchestration
Kubernetes
Used for deploying and managing NVIDIA NIM microservices.
Monitoring
Prometheus
Used for collecting metrics and enabling autoscaling decisions.
Visualization
Grafana
Used for visualizing performance metrics and monitoring application health.
Microservices
Nvidia Nim
Provides modular components for RAG deployment.
Key Actionable Insights
1Implementing Horizontal Pod Autoscaling (HPA) can significantly improve resource utilization and responsiveness of your applications during peak loads.By dynamically adjusting the number of pods based on real-time metrics, organizations can avoid overprovisioning and reduce costs while ensuring service quality.
2Monitoring latency metrics is essential for maintaining user experience in high-demand scenarios.Understanding how latency impacts user interactions allows teams to proactively scale resources and optimize performance, particularly for applications like chatbots.
3Using Prometheus for observability provides deep insights into application performance and resource usage.By integrating Prometheus metrics into your autoscaling strategies, you can make informed decisions about scaling and resource allocation based on actual usage patterns.
Common Pitfalls
1
Failing to monitor and adjust autoscaling thresholds can lead to performance degradation during peak loads.
Without proper monitoring, applications may either underperform or overconsume resources, leading to increased costs or poor user experience.
2
Neglecting to test autoscaling configurations under load can result in unexpected behavior during real-world usage.
It's crucial to simulate traffic and validate that autoscaling works as intended before deploying to production.
Related Concepts
Kubernetes Autoscaling Strategies
Performance Monitoring With Prometheus
Microservices Architecture Best Practices