The 60-second answer
Batch compatible requests to increase accelerator utilization while enforcing latency budgets. Use KV caching, efficient attention kernels, quantization, and model parallelism when model size or context requires it.
Build the answer in this order
1
Define the mechanism
Batch compatible requests to increase accelerator utilization while enforcing latency budgets.
2
Explain the architecture
Use KV caching, efficient attention kernels, quantization, and model parallelism when model size or context requires it.
3
Compare trade-offs
Control admission, queueing, timeouts, retries, and per-tenant quotas to protect tail latency.
4
Close with serving + evaluation
Monitor tokens/sec, time-to-first-token, inter-token latency, memory, queue depth, and failure rate.
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
- Continuous batching can improve throughput but complicates fairness and latency isolation.
- Capacity planning should model prompt and generation token distributions, not only requests per second.
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.