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. Prove that speculative decoding produces outputs from the same distribution as the target model.

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  2. What is the acceptance rate α in speculative decoding, and what factors influence it?

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  3. How does Medusa differ from a separate draft model approach? What are the trade-offs?

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  4. In what scenario would you not recommend speculative decoding?

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  5. Describe the EAGLE architecture and explain why it achieves higher acceptance rates than basic Medusa.

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  6. Explain PagedAttention. How does it differ from pre-allocated contiguous KV cache management?

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  7. How does prefix caching work, and what kind of application benefits most from it?

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  8. What is the attention sink phenomenon? Why does naïve sliding window attention fail for StreamingLLM?

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  9. How would you design a KV cache eviction policy for a system serving requests with highly variable lengths?

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  10. Explain KV cache quantization. Can you quantize the KV cache more aggressively than model weights? Why or why not?

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