DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference

Описание: Paper: DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference (2602.21548)
Published: 25 Feb 2026.

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arXiv: https://arxiv.org/abs/2602.21548
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This presentation explores how DualPath revolutionizes large language model inference for agentic applications by eliminating the storage bandwidth bottleneck. As AI agents engage in extended multi-turn conversations with massive context reuse, traditional architectures saturate prefill engine storage interfaces while decode engines sit idle. DualPath introduces a dual-path loading mechanism that aggregates bandwidth across all engines, combines workload-aware scheduling with traffic isolation, and delivers up to 1.87x throughput improvement in production deployments. The system demonstrates that the KV-Cache loading bottleneck is not fundamental but an artifact of suboptimal resource utilization.