Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Quantization & Inference Optimization, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain continuous batching and PagedAttention (vLLM) and how they improve GPU utilization
Describe speculative decoding and the accept/reject mechanism between draft and target models
Explain the prefill vs decode phase distinction and why they have different hardware bottlenecks
Compare serving frameworks (vLLM, TGI, TensorRT-LLM) on throughput, latency, and deployment complexity
Topics covered
Prefill vs DecodeKV CacheContinuous BatchingPagedAttentionvLLMSpeculative DecodingFlashAttentionAttention VariantsModel Parallelism for InferenceMemory EstimationQuantization and ThroughputServing MetricsServing FrameworksLatency Metrics
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When we talk about LLM inference, what are the two main phases, and how do they differ in what work the GPU is actually doing?
“What are the main trade-offs when choosing vLLM over HuggingFace Transformers for production serving? Are there scenarios where you might not pick vLLM?
“What is the difference between throughput and latency as serving metrics, and why do optimizations sometimes improve one at the expense of the other?