QuantSpec is a novel self-speculative decoding framework designed to accelerate the inference of Large Language Models, particularly in long-context scenarios. The system addresses memory and latency bottlenecks by employing a hierarchical 4-bit quantized KV cache and quantized weights, allowing a draft model to share the same architecture as the target model. This approach maintains a high token acceptance rate exceeding 90% while delivering end-to-end speedups of up to 2.5×. Additionally, the authors introduce a double full-precision buffer to store the most recent tokens, which prevents accuracy loss and minimizes the computational overhead of frequent re-quantization. By optimizing memory-bound attention operations, QuantSpec achieves superior performance and lower memory requirements compared to existing sparse-cache alternatives. The research demonstrates that integrating advanced quantization with speculative decoding can significantly enhance LLM scalability without sacrificing generation quality. Source: February 5, 2025QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV CacheUC Berkeley, Apple, ICSI, LBNLRishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholamihttps://arxiv.org/pdf/2502.10424