Structure your answer around the four dimensions the question asks for: computation, memory, attention behavior, and inference efficiency. For each, explain the core challenge and then briefly mention common mitigation strategies, showing you understand both the problem and the solution space.
Pro tip: Emphasize that the quadratic complexity of self-attention is the root cause, but also highlight that memory bandwidth and KV cache growth often become the practical bottlenecks in deployment, not just FLOPs. Mentioning real-world trade-offs (e.g., quality vs. speed) will set you apart.
Explain that self-attention computes pairwise interactions between all tokens, leading to O(n^2) time complexity in sequence length n. This makes training and inference prohibitively slow for very long contexts.
Describe how memory usage grows quadratically for attention matrices and linearly for KV cache during inference. This limits batch size and maximum context length due to GPU memory constraints.
Discuss how attention becomes diffuse over long sequences, making it hard to focus on relevant tokens. Also mention challenges with extrapolating positional encodings beyond training lengths.
Explain that long contexts increase per-token latency and reduce throughput due to larger KV cache and more attention computations. This impacts real-time applications and cost.
Summarize approaches like sparse attention, linear attention, sliding window, memory mechanisms, and efficient implementations (e.g., FlashAttention). Highlight trade-offs in quality, complexity, and hardware support.
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