What Is Model Serving?
Model serving is the process of deploying trained AI models into production systems where they can receive inputs and return predictions in real time. It encompasses the entire infrastructure stack needed to make a model accessible as a reliable service, including API endpoints, load balancing, hardware optimization, batching strategies, and monitoring. Effective model serving bridges the gap between data science experimentation and business value.
Key Considerations
Modern serving frameworks like vLLM, TGI (Text Generation Inference), and TensorRT optimize model execution through techniques such as continuous batching, KV-cache management, PagedAttention, and hardware-specific kernel optimizations. These optimizations can improve throughput by 10-50x compared to naive serving approaches.
Serving Architectures
Production model serving must address several critical dimensions: latency requirements (real-time vs batch), throughput (requests per second), availability (uptime guarantees), cost efficiency (GPU utilization), and scalability (handling demand spikes). Auto-scaling, canary deployments, and A/B testing capabilities are essential for enterprises managing multiple model versions.