163: 详解DeepSeekV4：Infra巨鲸、百万上下文走进现实、极致效率优化

DeepSeek V4 signifies a pivotal shift in large-scale model architecture, moving away from the MLA framework toward a hybrid attention mechanism that integrates sliding window and long-range attention. This release demonstrates the industry's transition toward engineering-heavy innovation, characterized by the simultaneous implementation of four complex features: a novel attention mechanism, the Muon optimizer, Multi-Head Connection (MHC), and FP4 training. By achieving an extremely low activation ratio and utilizing token-wise compression, DeepSeek effectively balances massive parameter capacity with computational efficiency. The reliance on custom kernels like Tailang and training-time pseudo-quantization highlights a broader trend where infrastructure mastery and the ability to manage coupled system complexities have become the primary differentiators for frontier AI labs. These advancements underscore a shift from simple scaling laws to highly optimized, cost-effective engineering paradigms that define the current competitive landscape of artificial intelligence.