Optimize GPU efficiency with OpenShift AI and llm-d flow-control

This blog post discusses optimizing GPU efficiency using llm-d flow control in OpenShift AI. It explains the noisy-neighbor problem in multi-tenant environments and demonstrates the benefits of prioritizing requests to improve model serving for varying workloads. The piece illustrates how flow control can enhance performance metrics like Time to First Token (TTFT) and end-to-end latency, providing detailed benchmarking results from tests on GPU resources. It includes practical implementation steps and highlights significant performance improvements even under saturation during concurrent requests.