Kimi开源新线性注意力架构，首次超越全注意力模型，推理速度暴涨6倍

Core Insights - The era of Transformers is being redefined with the introduction of the Kimi Linear architecture, which surpasses traditional attention models under the same training conditions [2][10]. Group 1: Kimi Linear Architecture - Kimi Linear employs a novel attention mechanism that reduces the KV cache requirement by 75% and achieves up to 6 times faster inference in long-context tasks [4][26]. - The architecture introduces Kimi Delta Attention (KDA), which allows for fine-grained control over memory retention, enabling the model to discard redundant information while preserving important data [12][10]. - KDA's state update mechanism is based on an improved Delta Rule, ensuring stability even with sequences of millions of tokens, preventing gradient explosion or vanishing [13][14]. Group 2: Performance and Efficiency - The model utilizes a 3:1 mixed layer design, combining three layers of linear attention followed by one layer of full attention, balancing global semantic modeling with resource efficiency [15]. - Kimi Linear has demonstrated superior performance across multiple benchmark tests, such as MMLU and BBH, outperforming traditional Transformers while maintaining accuracy in mathematical reasoning and code generation tasks [22][26]. - The architecture's deployment is seamless with existing vLLM inference frameworks, allowing for easy upgrades of Transformer-based systems to Kimi Linear [21]. Group 3: Industry Trends - The dominance of Transformers is being challenged, with alternative models like state space models (SSM) showing potential for efficient computation and long sequence modeling [28][30]. - Companies like Apple are exploring SSM architectures for their energy efficiency and lower latency, indicating a shift away from traditional Transformer reliance [30]. - The emergence of Kimi Linear signifies a move towards diverse innovations in AI architecture, suggesting a departure from the conventional Transformer path [32].