Artificial Intelligence • Interactive Module

Module 17: LLM Inference & Model Serving Engineering

Master high-throughput LLM inference and serving: vLLM, PagedAttention, continuous iteration-level batching, Ollama local runtime, Hugging Face TGI, post-training quantization (AWQ, GPTQ, GGUF), KV cache memory formulas, prefill vs decode profiling, and Server-Sent Events (SSE) progressive streaming.

Concept Overview & Direct Technical Answer

Direct Definition: Master high-throughput LLM inference and serving: vLLM, PagedAttention, continuous iteration-level batching, Ollama local runtime, Hugging Face TGI, post-training quantization (AWQ, GPTQ, GGUF), KV cache memory formulas, prefill vs decode profiling, and Server-Sent Events (SSE) progressive streaming.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/inference-serving. Free, open curriculum for software engineers, systems practitioners, and interview candidates.