Model Serving
Model serving is the process of deploying trained machine learning models and making them available for inference requests via an API or other interface. A model serving system handles model loading into GPU memory, request batching, response generation, and lifecycle management including versioning, updates, and retirement.
Model serving is the core runtime component of any AI inference platform. It sits between the API layer and the GPU hardware, managing how models are loaded, how requests are processed, and how responses are returned to users.
Modern model serving systems optimize for several metrics: latency (time to first token and time to complete), throughput (requests per second), GPU memory efficiency (fitting more models or larger batches), and reliability (handling failures gracefully).
Hoonify AI integrates with popular open-source model serving engines (vLLM, TGI) and adds multi-tenant management, billing integration, and GPU orchestration through TurbOS. Operators deploy models through the admin portal and tenants access them via the API.
Related Terms
See how model serving works in practice.
Explore the Platform