Title: Discussion 3 — Why Did Every Serving Engine Converge on the Same Architecture?
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 03 — Production Inference Fundamentals and the vLLM Engine

💬 Discussion 3 — Why Did Every Serving Engine Converge on the Same Architecture? (20 pts)

vLLM, Hugging Face's TGI, SGLang, and NVIDIA's TensorRT-LLM were built by different teams, at different companies and labs, on different timelines — yet they all ended up converging on strikingly similar architectural patterns: paged/block-based KV-cache management, continuous batching, and multi-GPU parallelism strategies. In a post of at least 200 words, answer: What do you think drove that convergence, and why has vLLM specifically become the reference standard that the field measures other engines against? Ground your answer in at least one specific concept from this week's lecture (PagedAttention, continuous batching, prefill/decode separation, or a parallelism strategy) rather than speaking only in generalities. Then respond to at least two classmates, engaging directly with a specific claim they made — agree, push back, or extend their reasoning with a detail from the readings.