In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning capabilities. However, this progress comes with a significant caveat: the inference process—generating responses one token at a time—remains a computational bottleneck. As LLMs grow in size and complexity, the latency and energy demands… →

LLMs have demonstrated exceptional capabilities, but their substantial computational demands pose significant challenges for large-scale deployment. While previous studies indicate that intermediate layers in deep neural networks can be reordered or removed without severely impacting performance, these insights have not been systematically leveraged to reduce inference costs. Given the rapid expansion of LLMs, which often… →

Large Language Models (LLMs) have revolutionized natural language processing (NLP) but face significant challenges in practical applications due to their large computational demands. While scaling these models improves performance, it creates substantial resource constraints in real-time applications. Current solutions like MoE Mixture of Experts (MoE) enhance training efficiency through selective parameter activation but suffer slower… →