A significant bottleneck in large language models (LLMs) that hampers their deployment in real-world applications is the slow inference speeds. LLMs, while powerful, require substantial computational resources to generate outputs, leading to delays that can negatively impact user experience, increase operational costs, and limit the practical use of these models in time-sensitive scenarios. As LLMs grow in size and complexity, these issues become more pronounced, creating a…
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