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Hossein Entezari Zarch

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#machine learning Preprint Sep 2026

ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference

This work proposes ASPIRE, a non-synchronized batched self-speculative decoding framework built on three components, which achieves speedup in decoding throughput over autoregressive baselines and improves average speedup by approximately $27\% over the strongest prior self-speculative baselines.

Amir Ziashahabi, Hossein Entezari Zarch, Lei Gao et al. · 0 citations