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

vLLM (Recommended Primary)

• PagedAttention memory management (20-30x throughput improvement)
• Continuous batching for dynamic request handling
• OpenAI-compatible API endpoints
• Use for: Most self-hosted LLM deployments

TensorRT-LLM

• Maximum GPU efficiency (2-8x faster than vLLM)
• Requires model conversion and optimization
• Use for: Production workloads needing absolute maximum throughput

Ollama

• Local development without GPUs
• Simple CLI interface
• Use for: Prototyping, laptop development, educational purposes

Decision Framework:

Self-hosted LLM deployment needed?
├─ Yes, need maximum throughput → vLLM
├─ Yes, need absolute max GPU efficiency → TensorRT-LLM
├─ Yes, local development only → Ollama
└─ No, use managed API (OpenAI, Anthropic) → No serving layer needed

ML Model Serving (Non-LLM)

BentoML (Recommended)

• Python-native, easy deployment
• Adaptive batching for throughput
• Multi-framework support (scikit-learn, PyTorch, XGBoost)
• Use for: Most traditional ML model deployments

Triton Inference Server

• Multi-model serving on same GPU
• Model ensembles (chain multiple models)
• Use for: NVIDIA GPU optimization, serving 10+ models

LLM Orchestration