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Near-Infinite Context

Near-infinite context refers to the capability of machine learning models, particularly transformer-based neural networks, to ingest and process extraordinarily long input sequences whose lengths scale directly with available distributed computing hardware rather than being constrained by single-device memory limits. In deep learning, standard self-attention mechanisms suffer from memory and computational bottlenecks that historically restricted context windows to a few thousand tokens. Near-infinite context architectures overcome these barriers by distributing sequence computations across networked processing units and overlapping communication with attention calculations, enabling models to handle contexts spanning millions of tokens without relying on approximations. This expansive contextual capacity allows artificial intelligence systems to maintain coherent understanding and perform unified reasoning across extensive code repositories, complete books, hours of high-resolution video, and long-horizon decision histories without truncating input data.

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Ring Attention with Blockwise Transformers for Near-Infinite Context

Ring Attention with Blockwise Transformers for Near-Infinite Context

Hao Liu, Matei Zaharia, Pieter Abbeel

OrganizationsUniversity of California Berkeley

Why you should read this

Overlaps the communication of key-value blocks with local computation over a mesh ring, theoretically allowing context lengths bounded only by the total cluster memory.

Part of:Prep reading for the model training infrastructure interview

Transformers have emerged as the architecture of choice for many state-of-the-art AI models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands imposed by Transformers limit their ability to handle long sequences, thereby posing challenges in utilizing videos, actions, and other long-form sequences and modalities in complex environments. We present a novel approach, Ring Attention with Blockwise Transformers (Ring Attention), which leverages blockwise computation of self-attention and feedforward to distribute long sequences across multiple devices while fully overlapping the communication of key-value blocks with the computation of blockwise attention. Our approach enables training and inference of sequences that are up to device count times longer than those achievable by prior memory-efficient Transformers, without resorting to approximations or incurring additional communication and computation overheads. Extensive experiments on language modeling and reinforcement learning tasks demonstrate the effectiveness of our approach in allowing millions of tokens context size and improving performance.

Added

2026-03-13