Author

Amir Ziashahabi

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Preprint Aug 2026

Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms

Long-context LLM decoding reads the key-value (KV) cache at every step. Loading it takes longer than computing attention over it, so throughput is bandwidth-bound. Hence, reducing the cache size can raise both decoding speed and serving capacity. The challenge is to reduce cache size while preserving the attention prod...

Samuel Fernández-Menduiña, Amir Ziashahabi, Eduardo Pavez et al. · 0 citations

#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