Explain FlashAttention, KV cache, and RoPE

Q: Explain FlashAttention, KV cache, and RoPE

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You are interviewing for an LLM-focused role.

FlashAttention
- Explain what problem it solves in transformer attention.
- Describe the high-level idea (how it reduces memory traffic) and its complexity implications.
- When would you expect the biggest speedups, and what are practical limitations?
KV Cache (Key/Value cache) in decoding
- Explain why KV caching is needed for autoregressive generation.
- What is stored, how it changes per generated token, and how it affects time/memory complexity.
- What are common optimizations (e.g., quantization, paging, chunking), and what trade-offs do they introduce?
RoPE (Rotary Positional Embeddings)
- Explain how RoPE encodes position information compared to absolute embeddings.
- Why does it help with extrapolation to longer contexts (relative position behavior)?
- How does it interact with attention computation (queries/keys rotation) and what are common variants/edge cases?

Explain FlashAttention, KV cache, and RoPE

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