LLM Serving Patterns

When to Use This Skill

Use this skill when:

• Designing LLM inference infrastructure
• Choosing between serving frameworks (vLLM, TGI, TensorRT-LLM)
• Implementing quantization for production deployment
• Optimizing batching and throughput
• Building streaming response systems
• Scaling LLM deployments cost-effectively

Keywords: LLM serving, inference, vLLM, TGI, TensorRT-LLM, quantization, INT8, INT4, FP16, batching, continuous batching, streaming, SSE, WebSocket, KV cache, PagedAttention, speculative decoding

LLM Serving Architecture Overview