Moonshot Launches Kimi Linear Architecture: KV Cache Reduced by 75%, Inference Speed Increased by 6 Times, Attention Mechanism Sees Groundbreaking Innovation!

Recently, Moonshot AI released a revolutionary technology called "Kimi Linear" hybrid linear attention architecture. This architecture is considered to be superior to traditional full-attention methods in various scenarios such as short-range, long-range processing, and reinforcement learning (RL). The core innovation lies in "Kimi Delta Attention" (KDA), which is an optimized upgrade of Gated DeltaNet, introducing a more efficient gating mechanism to improve the utilization efficiency of limited-state RNN (Recurrent Neural Network) memory.

The architecture of Kimi Linear is unique, consisting of three Kimi Delta Attention modules and one global MLA (Multi-Layer Perceptron). Through improvements to Gated DeltaNet, KDA can significantly compress the memory usage of limited-state RNNs through a fine-grained gating mechanism. This design not only increases the speed of model information processing but also effectively reduces memory consumption, offering stronger practicality.

Official data shows that under scenarios involving 1M tokens, the KV cache usage of Kimi Linear is reduced by 75%, and decoding throughput is increased up to six times. In terms of TPOT (training speed), compared to traditional MLA, Kimi Linear achieves a 6.3x acceleration. These significant performance improvements indicate the wide applicability of Kimi Linear in various AI tasks, especially in scenarios where speed and memory requirements are extremely high.

With the rapid development of artificial intelligence, enhancing the processing capability and efficiency of models has become a key challenge in the industry. Moonshot AI's Kimi Linear architecture offers new solutions for this field through its innovative design, and it may become a new industry benchmark in the future.

Detailed information about the Kimi Linear technical report can be obtained through the official GitHub page. Interested readers are encouraged to explore its technical details further.

Technical Report: https://github.com/MoonshotAI/Kimi-Linear/blob/master/tech_report.pdf