AI Infrastructure
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.
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AKS GPU node pools scale based on request queue depth with warm pools to avoid cold start latency. Request batching at the inference layer maximizes GPU utilization by grouping compatible requests. KV cache is stored in Redis Cache for multi-turn conversations, reducing redundant computation. AKS ingress routes requests to pods with available GPU memory, and KEDA autoscaling policies account for GPU utilization rather than CPU.
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