Prompt Cache: Modular Attention Reuse for Low Latency Inference
Abstract
We present Prompt Cache, an approach for accelerating inference for large language models (LLM) by reusing
attention states across different LLM prompts. Many input prompts have overlapping text segments, such as
system messages, prompt templates, and documents provided for context. Our key insight is that by precomputing
and storing the attention states of these frequently occurring text segments on the inference server, we can
efficiently reuse them when these segments appear in user prompts. Prompt Cache employs a schema to explicitly
define such reusable text segments, called prompt modules. The schema ensures positional accuracy during
attention state reuse and provides users with an interface to access cached states in their prompt. Using a prototype
implementation, we evaluate Prompt Cache across several LLMs. We show that Prompt Cache significantly reduce
latency in time-to-first-token, especially for longer prompts such as document-based question answering and
recommendations. The improvements range from 8× for GPU-based inference to 60× for CPU-based inference,
all while maintaining output accuracy and without the need for model parameter modifications.
attention states across different LLM prompts. Many input prompts have overlapping text segments, such as
system messages, prompt templates, and documents provided for context. Our key insight is that by precomputing
and storing the attention states of these frequently occurring text segments on the inference server, we can
efficiently reuse them when these segments appear in user prompts. Prompt Cache employs a schema to explicitly
define such reusable text segments, called prompt modules. The schema ensures positional accuracy during
attention state reuse and provides users with an interface to access cached states in their prompt. Using a prototype
implementation, we evaluate Prompt Cache across several LLMs. We show that Prompt Cache significantly reduce
latency in time-to-first-token, especially for longer prompts such as document-based question answering and
recommendations. The improvements range from 8× for GPU-based inference to 60× for CPU-based inference,
all while maintaining output accuracy and without the need for model parameter modifications.
Research Areas
