The Smart Way to Scale LLMs: Mastering HPA with Custom Metrics
Large Language Models (LLMs) are transforming industries, but deploying them effectively in a production environment presents a significant operational challenge. These models are notoriously resource-intensive, often requiring powerful and expensive GPU hardware to run efficiently. The core problem? Managing fluctuating demand without breaking the bank or compromising user experience.
Simply guessing the number of model instances you need—a practice known as static scaling—is a recipe for inefficiency. If you over-provision, you’re paying for idle GPU resources, a costly mistake. If you under-provision, your application will slow to a crawl during peak traffic, leading to high latency, request failures, and frustrated users.
The solution lies in intelligent, automated scaling. For teams using Kubernetes, the Horizontal Pod Autoscaler (HPA) is the go-to tool for this job. However, applying HPA to LLM workloads requires a more nuanced approach than you might use for a typical web application.
The Limits of Standard Autoscaling Metrics
By default, the Kubernetes HPA makes scaling decisions based on common metrics like CPU and memory utilization. For many applications, this works perfectly. If CPU usage across your pods climbs past 80%, HPA spins up new pods to distribute the load.
However, LLM inference workloads behave differently.
An LLM serving pod might show low average CPU usage, yet be completely saturated in its ability to handle concurrent requests. The true bottleneck is often the model’s capacity to process simultaneous inference tasks on the GPU. A pod might only be able to handle one or two requests at a time. If ten requests arrive at once, eight will be stuck in a queue, waiting. Standard CPU and memory metrics are blind to this critical queueing problem, leaving your autoscaler unable to react to the actual user demand.
Custom Metrics: The Key to Efficient LLM Scaling
To scale LLMs effectively, you must move beyond standard metrics and base your scaling decisions on what truly matters: the workload itself. This is where HPA with custom metrics becomes a game-changer.
Instead of tracking CPU usage, you can configure HPA to monitor metrics that directly reflect the application’s load and performance. The most effective custom metrics for LLM workloads include:
- Concurrent In-Flight Requests: This is arguably the most accurate metric. It measures exactly how many requests a pod is actively processing at any given moment. If you know a single pod can only handle two requests concurrently, you can set your HPA to scale up whenever the average in-flight requests per pod exceeds one.
- Requests Per Second (RPS): A classic and highly effective metric that tracks the rate of incoming requests. As RPS climbs, HPA can proactively add more pods to meet the anticipated demand.
- Queue Depth: This metric measures the number of requests that have been received but are waiting to be processed. A growing queue is a direct indicator that your system is under-provisioned and that users are experiencing increased latency. Scaling based on queue depth allows you to add capacity precisely when it’s needed to keep wait times low.
By using these custom metrics, your scaling decisions become directly tied to the user experience. You are no longer guessing based on secondary indicators like CPU; you are responding to the real-time demand placed on your model.
How to Implement Custom Metric-Based HPA
Setting up HPA with custom metrics involves a few key steps. While the exact implementation details depend on your specific stack, the general workflow is consistent.
- Instrument Your Application: Your LLM serving application must be modified to expose the custom metric you want to use. This is typically done by creating a
/metricsendpoint that a monitoring system like Prometheus can scrape. - Deploy a Metrics Server: Kubernetes HPA doesn’t natively collect custom metrics. You need an adapter that can provide these metrics to the HPA controller. The most common solution is the Prometheus Adapter, which takes metrics collected by Prometheus and exposes them to the Kubernetes API. Alternatives like KEDA (Kubernetes Event-driven Autoscaling) also provide powerful capabilities for this.
- Configure the HPA Object: Finally, you define your HPA resource in a YAML file, specifying your custom metric as the scaling target. Instead of
cpuormemory, you will reference your custom metric (e.g.,http_requests_in_flight) and set a target average value. HPA will then handle the rest, automatically adjusting the number of pods to keep the metric at your desired level.
The Tangible Benefits of Intelligent Scaling
Adopting a custom metrics strategy for scaling your LLM workloads delivers clear and powerful advantages:
- Drastic Cost Reduction: By automatically scaling down GPU-powered pods during periods of low traffic, you eliminate waste and pay only for the resources you actually use. This can lead to massive savings, especially with expensive GPU instances.
- Superior Performance and Reliability: Your application can seamlessly scale up to handle sudden traffic spikes, ensuring low latency and a consistently positive user experience. This prevents bottlenecks that cause requests to time out or fail.
- Streamlined Operations: Automating scaling decisions removes the burden of manual intervention and guesswork from your operations team. Your system becomes more resilient and self-sufficient, allowing your engineers to focus on more strategic tasks.
As LLMs become more integrated into business-critical applications, running them efficiently and cost-effectively is no longer optional. While the standard Kubernetes HPA provides a solid foundation, mastering it with custom metrics is the definitive approach for achieving performance, reliability, and financial control over your demanding LLM workloads.
Source: https://collabnix.com/horizontal-pod-autoscaling-for-llm-workloads-2/
