June 7, 2024, 4:44 a.m. | Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao

cs.LG updates on arXiv.org arxiv.org

arXiv:2401.10774v2 Announce Type: replace
Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue, their implementation is impeded by the challenges associated with acquiring and maintaining a separate draft model. In this paper, we present …

abstract accelerator arxiv auto bandwidth cache computation cs.cl cs.lg decoding framework hbm inference language language models large language large language models llm llms memory moving multiple output parameters replace simple type while

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