Compare LLM inference engines: vLLM, SGLang, TensorRT-LLM, TGI, and llama.cpp. What actually differs?
Anyone can list the engines. The signal is naming the one mechanism that distinguishes each (paged KV blocks, a radix prefix tree, ahead-of-time kernel compilation, quantized CPU inference) and picking from your traffic shape rather than a leaderboard.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Anyone can list the engines. The signal is naming the one mechanism that distinguishes each (paged KV blocks, a radix prefix tree, ahead-of-time kernel compilation, quantized CPU inference) and picking from your traffic shape rather than a leaderboard.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
