ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference

arXiv:2505.19342v2 Announce Type: replace Abstract: Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device bandwidth, making them impractical for bandwidth-constrained environments. We present ASTRA, a communication-efficient framework that integrates sequence parallelism with mixed-precision attention, where non-local token embeddings are transmitted as low-bit vector-quantized codes while local attention remains full precision. To preserve accuracy under aggressive compression, ASTRA introduces Noise-Augmented Q

Source: arXiv cs.LG — read the full report at the original publisher.

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