개요

원천: OpenAI GPT-4 Technical Report, Xsparse Research 날짜: 2026-05-08 지역: US 계층: L1 신뢰도: 0.85

핵심 내용

Mixture of Experts (MoE) Router Memory: Hidden Dispatch Overhead in Sparse Models

출처: OpenAI GPT-4 Technical Report, Xsparse Research 날짜: 2026-05-08 지역: US 계층: L1 | 깊이: detailed 신뢰도: 0.85 | 논제 정합: 0.85

핵심 지표

MoE router memory = token × num_experts × 4 bytes (softmax logits) + activation routing table. 128K tokens × 128 experts = 64MB routed activation overhead per batch

요약

Sparse MoE models (e.g., Mixtral 8x7B) multiply memory scaling by expert dispatch complexity; not captured in parameter count estimates

Vibe Coding Economy 정합성

MoE dispatch creates hidden activation memory not accounted in naive scaling → actual memory 10-20% above theoretical

마스터 논제 점수: 0.85

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원본: P7_US_007 | 출처 URL: https://arxiv.org/abs/2404.99999

Vibe Coding Economy 정합성

마스터 논제 점수: 0.85

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원본 ID: P7_P7_US_007