国产类脑大模型适配国产沐曦GPU！长序列推理提速超百倍，仅用2%数据匹敌主流模型

Core Insights - The article discusses the development of SpikingBrain-1.0, a brain-inspired large model that aims to reduce the computational costs associated with long sequence reasoning [1][2]. Group 1: Model Architecture and Performance - SpikingBrain-1.0 leverages a brain-like information processing mechanism, achieving linear/near-linear complexity, which significantly enhances speed for long sequences. For instance, it shows a 26.5x speed improvement on a 1M length sequence compared to mainstream models [2][18]. - The model is designed to be compatible with domestic GPU clusters, indicating the feasibility of creating a new ecosystem for non-Transformer large models in China [2][28]. - The architecture includes SpikingBrain-7B and SpikingBrain-76B, which utilize a linear (mixed) model structure and a hybrid linear MoE model, respectively [10][14]. Group 2: Theoretical Foundations - The research team has established that complex endogenous dynamics in spiking neurons can mathematically equate to combinations of simpler spiking neurons, suggesting the potential for smaller networks to replace larger ones [5][6]. - A new approach based on "endogenous complexity" is proposed, aiming to integrate the rich dynamical characteristics of biological neurons into model development [7][8]. Group 3: Efficiency and Training - SpikingBrain-1.0 demonstrates significant training efficiency for long sequences, achieving comparable performance to many open-source Transformer models with only about 2% of the data [18]. - The model supports multi-card parallel inference and can handle up to 4M length sequences, with substantial acceleration in time-to-first-token (TTFT) compared to standard attention mechanisms [21][22]. Group 4: Future Directions - The team aims to further explore the relationship between endogenous dynamics of neurons and foundational AI operators, seeking to bridge neuroscience and artificial intelligence [28]. - The model is expected to provide significant efficiency advantages in scientific tasks involving long sequences, such as complex multi-agent simulations and molecular dynamics [28].