  Self-attention is an essential component of large language models (LLM) but a
significant source of inference latency for long sequences. In multi-tenant LLM
serving scenarios, the compute and memory operation cost of self-attention can
be optimized by using the probability that multiple LLM requests have shared
system prompts in prefixes. In this paper, we introduce ChunkAttention, a
prefix-aware self-attention module that can detect matching prompt prefixes
across multiple requests and share their key/value tensors in memory at runtime
to improve the memory utilization of KV cache. This is achieved by breaking
monolithic key/value tensors into smaller chunks and structuring them into the
auxiliary prefix tree. Consequently, on top of the prefix-tree based KV cache,
we design an efficient self-attention kernel, where a two-phase partition
algorithm is implemented to improve the data locality during self-attention
computation in the presence of shared system prompts. Experiments show that
ChunkAttention can speed up the self-attention kernel by 3.2-4.8$\times$
compared to the state-of-the-art implementation, with the length of the system
prompt ranging from 1024 to 4096.
