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Transformer Self-Attention의 KV Cache 기본 원리부터 MHA, MQA, GQA(Llama 2/3), MLA(DeepSeek-V2/V3) 메커니즘의 메모리 분석과 비교, KV Cache 압축 기법(양자화, 퇴거 정책, 슬라이딩 윈도우), PagedAttention(vLLM) 구현, PyTorch 코드 예제, OOM 장애 사례와 최적화 체크리스트를 다룹니다.
Attention-mechanism
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- Published on
FlashAttention 시리즈(v1~v3) 핵심 논문 분석. IO-Aware 알고리즘의 tiling 전략, GPU SRAM/HBM 메모리 계층 활용, 역전파 recomputation, FlashAttention-2의 병렬화 개선, FlashAttention-3의 FP8 지원과 비동기 파이프라이닝까지 실전 벤치마크와 함께 다룹니다.