KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation

arXiv:2405.05329v1 Announce Type: cross
Abstract: Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent tokens. In this work, we propose an efficient parallelization scheme, KV-Runahead to accelerate the prompt phase. The key observation is that the extension phase generates tokens faster than the prompt phase because of key-value cache (KV-cache). Hence, KV-Runahead parallelizes the prompt phase by orchestrating multiple processes …

abstract arxiv cache causal cs.ai cs.cl cs.dc decoding extension generate inference key language language model large language large language model llm parallelization prompt scalable the prompt token tokens type value work