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A Shape-Adaptive Architecture with Disaggregated Quantization for Efficient LLM Serving
Large language models (LLMs) have become the backbone of modern AI applications, but pose significant challenges for efficient inference. Their autoregressive generation divides execution into two phases: prefill, dominated by large GEMMs, and decoding, dominated by small GEMVs. Modern serving syste
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