configuration
AI infra interview questions tagged configuration, across every topic.
4 questions · 2 unlocked for you
Concepts behind "configuration"
The curriculum that explains the ideas these questions test.
Foundational
vLLM Server Arguments That MatterA vLLM deployment is mostly decided by a dozen flags, and the ones that matter fall into four groups: how the model is split across GPUs, how memory is divided between weights and cache, how requests are batched, and which specialized backends the model needs. Getting the first two wrong produces an engine that will not start or that runs out of memory under load. Getting the third wrong produces an engine that starts, serves, and misses its latency target by a wide margin.🧮 Open Weights & Serving Engines
Foundational
SGLang Server Arguments That MatterSGLang's tuning model is different from vLLM's in one way that matters: it exposes the scheduler's aggressiveness and the static memory fraction as direct knobs, and its own documentation gives target values for the runtime signals those knobs move. That makes tuning it a measurement loop rather than guesswork. Aim for a queue of a hundred to a couple of thousand requests, token usage above 0.9, and five to eight gigabytes of free GPU memory after startup, then adjust the flags that move each one.🧮 Open Weights & Serving Engines