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Attention scores decay with distance. By position 50K, tokens may have near-zero influence. Positional encodings have practical limits, regardless of window size.

Full attention is O(n²). Sliding window attention is O(n). The trade: lose long-range dependencies, gain linear scaling. Often worth it.

Self-attention lets a sequence talk to itself. Cross-attention lets one sequence attend to another. Understanding the difference enables better architectures.

Most queries don't need the full context. Selecting the right 12% often preserves 95% of quality at a fraction of the cost and latency.

Models advertise 128K context windows. But attention quality degrades with distance. The last 10% of context often contributes less than the first 10%.

Optimizing for compute when you're memory bound wastes effort. Optimizing for memory when you're compute bound wastes opportunity. Profile first, then optimize.

Tensor parallelism cuts latency by splitting layers across GPUs. Pipeline parallelism increases throughput by splitting the model into stages. Choose based on your constraint.

Four GPUs don't give you 4x throughput. Communication overhead, load imbalance, and synchronization eat into gains. Know the scaling curve before you buy.

Four GPUs don't give you 4x the KV cache memory. Communication overhead, activation memory, and synchronization eat into the gains. Plan accordingly.

Every configuration lives on a quality-cost curve. Some are on the efficient frontier, most aren't. Map the frontier, then choose your spot deliberately.