When would you choose vLLM vs SGLang vs TensorRT-LLM vs a custom stack?
Reported at:NVIDIATogether AI
Model answer
A concise answer you could give in the room. Try your own version first, then compare.
What the interviewer is probing
Whether you can choose an inference engine pragmatically, based on workload, hardware, features and team capacity rather than benchmarks alone.
How to structure your answer
- 1Define the workload: model size, traffic shape, latency SLOs, structured output, multi-LoRA.
- 2Compare engines: vLLM (broad, PagedAttention), SGLang (RadixAttention, structured programs), TensorRT-LLM (peak NVIDIA performance), llama.cpp (edge).
- 3Weigh the operational cost: compile steps, upgrades, observability, hardware lock-in.
- 4Benchmark on your own traffic before you commit.
Key points to hit
0/3 revealedTry to answer each one out loud before revealing it.
What does TensorRT-LLM do to a model?
When should you build a custom stack?
What decides vLLM versus SGLang?
Common pitfalls
- Choosing on a single synthetic benchmark.
- Ignoring the upgrade and model-support cadence.
- Forgetting autoscaling and cold-start time.
Likely follow-ups
- How would you serve 100 LoRA adapters?
- How do you autoscale GPU inference?
- What would you monitor in production?
Source: Outcome School · ai-engineering-interview-questions-company-wise. Question compiled by Outcome School from publicly reported interview experiences (Apache-2.0). The guidance on this page is original.