How do you implement model serving infrastructure for offline AI at scale?
Offline AI model serving at organizational scale requires infrastructure for model management, request routing, and resource optimization. Architecture: model registry (catalog of available models with versions, hardware requirements, and performance characteristics), model server (inference engine handling requests: vLLM, TGI, Triton Inference Server, or llama.cpp server), load balancer (distribute requests across multiple model instances), and monitoring stack (track latency, throughput, errors, and resource utilization). vLLM: production-grade serving with PagedAttention (efficient KV-cache management), continuous batching (batch incoming requests dynamically for throughput), speculative decoding (use smaller draft model to speed generation), and OpenAI-compatible API. Triton Inference Server: supports multiple model frameworks simultaneously (ONNX, TensorRT, PyTorch), implements dynamic batching, model versioning, and model ensemble pipelines. Scaling: horizontal scaling with multiple GPU servers behind a load balancer, implement model-aware routing (route requests to servers with the appropriate model loaded), and use request queuing with priority levels. Multi-model serving: load popular models permanently in memory, implement LRU model swapping for less-frequent models, use model multiplexing to share GPU resources across multiple small models. Infrastructure as Code: define model deployments using Kubernetes manifests or Docker Compose, implement GitOps for model deployment changes, and automate model deployment pipelines. Cost optimization: right-size GPU instances for actual workload, implement auto-scaling based on request queue depth, use spot/preemptible instances for batch workloads, and schedule resource-intensive tasks during off-peak hours.