In Depth
Model serving is the process of making trained AI models available for inference in production environments. It encompasses loading models into memory, managing GPU resources, handling incoming requests, batching for efficiency, scaling to meet demand, and returning predictions with acceptable latency. It is the critical bridge between model development and real-world value creation.
Modern model serving platforms like TorchServe, TensorFlow Serving, Triton Inference Server, and vLLM handle the complex engineering challenges of production AI. These include dynamic batching (grouping multiple requests to maximize GPU utilization), model versioning (seamlessly updating models without downtime), auto-scaling (adjusting resources based on demand), and health monitoring.
For large language models, serving presents unique challenges. LLMs require significant GPU memory, generate tokens sequentially (limiting throughput), and have variable-length outputs. Specialized serving frameworks have emerged to address these challenges, implementing techniques like continuous batching, PagedAttention, speculative decoding, and prefix caching. The cost of serving LLMs at scale is a major business consideration, often exceeding the one-time cost of training.