This AI Paper from UC Berkeley Introduces Pie: A Machine Learning Framework for Performance-Transparent Swapping and Adaptive Expansion in LLM Inference

Using large language models (LLMs) has revolutionized artificial intelligence applications, enabling breakthroughs in natural language processing tasks like conversational AI, content generation, and automated code completion. Often with billions of parameters, these models rely on massive memory resources to store intermediate computation states and large key-value caches during inference. These models’ computational intensity and growing […]

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