LLM推理系统问答

题集来源:阿东玩 AI(Adong)收录项目:AgentGuide查看原题集

题目目录

70 道原题
  1. Explain Multi-Query Attention vs. Grouped Query Attention. Why are they important for inference?

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  2. Walk me through what happens to the KV cache during a 1000-token generation on a 7B model.

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  3. What is the attention complexity bottleneck and how do modern systems address it?

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  4. How does RoPE enable context length extrapolation? What goes wrong if you naively extend context beyond training length?

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  5. Why is the decode phase memory-bandwidth-bound while prefill is compute-bound? What are the implications for serving system design?

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  6. You need to deploy a 70B LLM on 2 A100 80GB GPUs. What quantization strategy would you choose and why?

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  7. Explain the difference between weight-only quantization and weight-and-activation quantization. When does each apply?

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  8. What is the Straight-Through Estimator and why is it needed for QAT?

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  9. How does SmoothQuant address the activation outlier problem? What are its limitations?

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  10. Compare GPTQ and AWQ. Which would you use in production and under what conditions?

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