Production LLM serving on Azure with AKS GPU workers, request batching, KV cache management, and streaming token delivery.
Difficulty: advanced
Tags: ai, llm, inference, gpu, serving, azure
Serving large language models in production requires managing GPU memory, batching requests for throughput, streaming tokens for low time-to-first-token, and caching KV states for multi-turn conversations. This Azure-native pipeline uses AKS with GPU node pools for inference workers, Azure Redis Cache for KV state caching, and Event Hubs for streaming token delivery — all while maintaining sub-second latency. Built for ML platform teams serving large language models in production with sub-second latency and cost-optimized GPU utilization.