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Mixture-of-Channels: Exploiting Sparse FFNs for Efficient LLMs Pre-Training and Inference

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of the Mixture-of-Channels (MoC) architecture marks a significant advancement in the efficiency of large language models (LLMs). As LLMs have grown in size and complexity, they have faced substantial memory overhead, particularly from feed-forward networks (FFNs), which has hindered both training and inference processes. MoC addresses this issue by conducting detailed memory profiling and selectively activating only the Top-K most relevant channels per token using SwiGLU's gating mechanism. This innovative approach not only reduces activation memory but also enhances inference efficiency by minimizing memory access through partial weight loading into GPU SRAM. Extensive experiments have validated that MoC delivers significant memory savings and throughput gains while maintaining competitive model performance. This development is particularly important as it aligns with the ongoing efforts to optimize LLMs, ensuring they can scale effectively without incurring prohibiti…
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