Model Serving

Purpose

Deploy LLM and ML models for production inference with optimized serving engines, streaming response patterns, and orchestration frameworks. Focuses on self-hosted model serving, GPU optimization, and integration with frontend applications.

When to Use

• Deploying LLMs for production (self-hosted Llama, Mistral, Qwen)
• Building AI APIs with streaming responses
• Serving traditional ML models (scikit-learn, XGBoost, PyTorch)
• Implementing RAG pipelines with vector databases
• Optimizing inference throughput and latency
• Integrating LLM serving with frontend chat interfaces

Model Serving Selection

LLM Serving Engines