About this document
Accelerating Large-Scale Reasoning Model Inference Self-Speculative Decoding With Sparse Attention by y3667573 is a document available to read on EtoBox.
The document presents SparseSpec, a speculative decoding framework designed to enhance the efficiency of reasoning language models (RLMs) by addressing memory bandwidth bottlenecks during token generation. It utilizes a novel sparse attention mechanism, PillarAttn, and incorporates system optimizations such as a unified scheduler and dynamic KV-Cache management, achieving up to 2.13× throughput improvements over existing solutions. SparseSpec is open-sourced and demonstrates significant performance gains ac
- Author
- y3667573
- Language
- EN