The 60-second answer
Quantization lowers weight or activation precision to reduce memory and potentially improve throughput. Compare post-training quantization with quantization-aware training based on quality tolerance and retraining budget.
Build the answer in this order
1
Define the mechanism
Quantization lowers weight or activation precision to reduce memory and potentially improve throughput.
2
Explain the architecture
Compare post-training quantization with quantization-aware training based on quality tolerance and retraining budget.
3
Compare trade-offs
Evaluate per-channel vs per-tensor schemes and outlier-sensitive layers.
4
Close with serving + evaluation
Benchmark latency, memory, and quality on the actual target hardware.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
01Definition
02Mechanism
03Trade-offs
04Failure modes
05When to use
Senior-level signal
- KV-cache precision can matter as much as weight precision for long contexts.
- Use layer-wise sensitivity instead of applying one precision uniformly.
What the interviewer is really testing
Understanding beyond prompting: architecture, retrieval, evaluation, inference, safety, latency, cost, and failure recovery.
Likely follow-up questions
What assumption makes this approach work?
When would you choose the strongest alternative instead?
What production or data failure mode changes your answer?
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.