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Performance optimization follows a systematic approach: establish budgets, profile to find bottlenecks, and iterate. Start with low-hanging fruit like removing inefficiencies and using GPU instancing. Optimize data layout for cache-friendly access patterns and CPU predictability. Reduce work by culling invisible objects and using spatial partitioning. Break hardware limits through cheating techniques like LODs, impostors, and dynamic quality scaling. The key is balancing average and worst-case performance while understanding hardware constraints.