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
The increasing scale of Transformer models demands more efficient distributed inference methods, especially for environments with limited bandwidth, driving innovation in communication-efficient acceleration.
Reducing communication overhead in multi-device Transformer inference can unlock new applications and deployment scenarios, making advanced AI more accessible and cost-effective.
Hardware constraints on inter-device bandwidth become less of a bottleneck for large-scale AI model deployment, potentially broadening the adoption of powerful AI systems beyond high-end data centers.
- · AI cloud providers
- · Edge AI chip manufacturers
- · Developers of large AI models
- · Bandwidth-constrained environments
- · Companies relying solely on high-bandwidth infrastructure for AI deployment
Transformer models can be deployed more efficiently across distributed, potentially lower-bandwidth, hardware setups.
This could lead to a proliferation of more powerful AI applications at the edge or in hybrid cloud environments.
Increased accessibility of advanced AI might accelerate the development of next-generation AI agents and autonomous systems.
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Read at arXiv cs.LG