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As the race to scale large language models (LLMs) continues, there's growing recognition that simply increasing model size and data may not be enough. The core attention mechanism in models like GPT has a quadratic computational cost, meaning that as input sequences grow longer, the costs—both in terms of computation and memory—grow exponentially, creating a bottleneck for scaling. This challenges the assumption that bigger models always equal better performance. With the limitations of self-attention becoming more apparent, the future of LLMs may lie not in sheer scale, but in more efficient architectures that rethink how models handle long sequences. This article explores the math behind these challenges and the potential for innovative solutions that could reshape the future of AI.
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