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Production LLM model serving

LLM Inference Pipeline

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.

Data Flow

Inference API
Request Router
Model Weights
Inference Workers (GPU)
Token Stream
KV Cache
Usage Tracking

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Service Breakdown (7 services)

Other7 services
Inference API
  • •Exposes backend services through managed API endpoints
  • •Enforces authentication, throttling, and quotas
  • •Provides developer portal and API analytics
Request Router
  • •Executes event-driven functions without managing servers
  • •Scales based on event volume with consumption billing
  • •Supports durable functions for stateful workflows
Inference Workers (GPU)
  • •Runs LLM inference on GPU-accelerated node pools
  • •Batches requests to maximize GPU throughput
  • •Supports model sharding across multiple GPUs
KV Cache
  • •Caches frequently accessed data in-memory
  • •Reduces database round-trips and latency
  • •Supports TTL-based expiration policies
Usage Tracking
  • •Provides globally distributed multi-model database
  • •Guarantees single-digit ms reads worldwide
  • •Supports five consistency levels
Model Weights
  • •Stores serialized model artifacts and checkpoints
  • •Serves weights to inference workers on startup
  • •Manages versioned model binaries for rollback
Token Stream
  • •Captures millions of events per second
  • •Supports real-time and batch processing
  • •Integrates with stream analytics pipelines

Scaling Strategy

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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