llm-inference-scaling

To scale LLM inference on Kubernetes, you can use KEDA combined with Prometheus metrics to dynamically adjust pod counts based on real-time traffic and custom GPU utilization. This setup ensures high availability during unpredictable user request spikes.

Yes, you can configure KEDA to scale vLLM deployments by monitoring waiting requests and GPU cache usage via Prometheus. This queue-based scaling mechanism prevents service degradation during sudden traffic spikes.

To optimize GPU compute costs for AI workloads, integrate spot instances into your Kubernetes cluster. This strategy significantly reduces expenses while maintaining dynamic scaling capabilities for your LLM inference pods.

Yes, you need Prometheus configured with GPU metrics to effectively autoscale LLM inference clusters. It provides the real-time resource utilization and queue data required by KEDA to trigger horizontal pod autoscaling.

GPU-aware autoscaling works by leveraging custom GPU metrics from Prometheus to inform KEDA. Instead of relying solely on CPU or memory, it scales vLLM inference pods based on actual GPU cache usage and waiting request queues.