手机实现GPT级智能，比MoE更极致的稀疏技术：省内存效果不减｜对话面壁&清华肖朝军

Core Viewpoint - The balance between computing power and efficiency is crucial in the competition of large models, with innovative architectures emerging to address these challenges [1]. Group 1: Model Deployment and Efficiency - Edge deployment has been a significant challenge for large models due to computing power bottlenecks [2]. - The approach taken by Mianbi Intelligence and Tsinghua University involves neuron-level sparse activation, which significantly reduces resource consumption while maintaining model performance [3][4]. - Configurable Foundation Models (CFM) utilize inherent sparse activation properties of models, greatly enhancing parameter efficiency compared to Mixture of Experts (MoE) [6][7]. Group 2: Parameter Efficiency and Model Comparison - Parameter efficiency refers to the effectiveness of model parameters, impacting memory usage, especially in mobile applications where memory is limited [7]. - CFM emphasizes finer granularity in sparsity at the neuron level, contrasting with MoE's expert-level sparsity, making CFM more suitable for edge applications [8][11]. - MoE's fixed activation of experts limits its flexibility, while CFM's dynamic activation allows for better adaptability to task complexity [11][9]. Group 3: Model Architecture and Future Directions - The current optimization paths for model architectures include linear models like Mamba and RWKV, and transformer-based models with improved key-value cache management [14]. - While some linear models have shown competitive performance against transformers, they still face challenges in long-text evaluations [16][18]. - The emergence of new architectures may depend on their ability to leverage hardware effectively, as seen with transformers' design for GPU utilization [18][19]. Group 4: Model Size and Compression - Small models are currently defined as being in the range of 2-3 billion parameters for edge applications, with ongoing research into compression limits [21][24]. - The essence of intelligence may not solely be compression, but rather the ability to learn and abstract knowledge effectively [23]. Group 5: Long-Form Reasoning and Innovation - The development of long reasoning chains (CoT) is seen as a critical area for future breakthroughs in model capabilities [32]. - Current models struggle with the complexity of long-form reasoning, and there is a need for innovative approaches to enable AI to generate novel ideas beyond existing knowledge [35][36].