  This work introduces an efficient method to scale Transformer-based Large
Language Models (LLMs) to infinitely long inputs with bounded memory and
computation. A key component in our proposed approach is a new attention
technique dubbed Infini-attention. The Infini-attention incorporates a
compressive memory into the vanilla attention mechanism and builds in both
masked local attention and long-term linear attention mechanisms in a single
Transformer block. We demonstrate the effectiveness of our approach on
long-context language modeling benchmarks, 1M sequence length passkey context
block retrieval and 500K length book summarization tasks with 1B and 8B LLMs.
Our approach introduces minimal bounded memory parameters and enables fast
streaming inference for LLMs.
