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About this interview
A technical interview on Quantization & Inference Optimization, pitched at the medium 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 the difference between post-training quantization and quantization-aware training
Compare INT8 absmax/zero-point quantization against GPTQ, AWQ, and GGUF approaches
Describe the outlier problem in activation quantization and how SmoothQuant addresses it
Explain the memory and throughput trade-offs at 4-bit versus 8-bit versus 16-bit precision
Topics covered
quantization basicsnumeric formatsmemory estimationPTQ vs QATabsmax quantizationzero-point quantizationweight vs activation quantizationoutlier problemLLM.int8quantization granularityGPTQGPTQ HessianAWQGGUF and llama.cpp
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In a sentence or two, what is quantization in the context of large language models, and why has it become so important?
“What is the GGUF format and why did it become popular for running LLMs on consumer hardware and CPUs?
“How does mixed-precision quantization work, and why would you quantize some layers more aggressively than others within the same model?