Prioritize cache performance by leveraging proven optimization techniques and avoiding performance anti-patterns. Use standard caching decorators like @lru_cache for functions that benefit from memoization, implement zero-copy operations when moving data between cache layers, and avoid unnecessary operations that degrade performance. Key practices: - Apply @lru_cache decorator to functions with reusable results - Implement zero-copy techniques for cache data transfers - Avoid additional slice operations or data transformations in cache paths - Choose cache architectures that minimize operational overhead Example: ```python from functools import lru_cache @lru_cache(maxsize=128) def should_use_flashinfer_cutlass_moe_fp4_allgather(): # Expensive computation cached automatically return compute_moe_config() # Zero-copy backup for Mooncake backend if self.is_mooncake_backend(): # Use zero-copy operations instead of generic page backup self.zerocopy_page_backup(operation) ``` This approach ensures cache operations remain performant under load while leveraging battle-tested optimization patterns that reduce computational overhead and memory copying.