<ul><li>Transformer models face challenges with long-context inference due to quadratic time and linear memory complexity.</li><li>Recurrent Memory Transformers (RMTs) address this by reducing the cost to linear time and constant memory usage, but suffer from a sequential execution bottleneck.</li><li>Diagonal Batching is introduced as a scheduling scheme in RMTs to enable parallelism across segments, enhancing GPU inference efficiency without the need for complex batching techniques.</li><li>Implementing Diagonal Batching in ARMT model leads to significant speedups, strengthening the practicality of RMTs for real-world applications with long contexts.</li></ul>

Diagonal Batching Unlocks Parallelism in Recurrent Memory Transformers for Long Contexts

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