LLMOps 与生产应用

题集来源:AMIT SHEKHAR(amitshekhariitbhu)收录项目:ai-engineering-interview-questions查看原题集 反馈(在 GitHub 新标签页打开)

题目目录

53 道原题
  1. How does Prompt Caching work?

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  2. Prefill vs Decode

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  3. Why is prefill compute-bound and decode memory-bandwidth-bound?

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  4. What is chunked prefill, and why does it improve tail latency under mixed traffic?

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  5. What is Prefill-Decode Disaggregation, and when does it pay off?

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  6. Explain the AI product lifecycle from ideation to production.

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  7. What is LLMOps, and how does it differ from traditional MLOps?

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  8. How do you serve LLMs in production?

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  9. What is model quantization?

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  10. Explain post-training quantization (PTQ) vs quantization-aware training (QAT). What breaks when you push weights to 2-4 bits?

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