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Gleam framework enables efficient cross-device GPU sharing for AI inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Gleam, a framework designed to enable efficient GPU sharing across devices within local area networks for AI inference. Gleam addresses network bottlenecks by implementing automatic model weight caching for reduced bandwidth, asynchronous execution to mitigate latency from frequent API calls, and a dynamic runtime task scheduler that optimizes API remoting pairs based on network conditions and GPU contention. The system also ensures CUDA context consistency across distributed executions. Experiments demonstrate that Gleam significantly outperforms existing methods in API remoting efficiency and overall system throughput on various AI workloads and NVIDIA GPUs. AI

IMPACT Could enable more widespread and efficient AI inference on consumer hardware by leveraging distributed GPUs.

RANK_REASON The item is a research paper detailing a new framework for GPU sharing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gleam framework enables efficient cross-device GPU sharing for AI inference

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The item is a research paper detailing a new framework for GPU sharing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhihao Xu, Hao Zhong, Zeting Zhou, Yuhang Xu, Haoyu Tong, Wei Wang, Jinshan Chen, Keqiang He, Chong Zhu, Shengzhong Liu, Fan Wu, Guihai Chen ·

    Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs

    arXiv:2607.23115v1 Announce Type: cross Abstract: This paper aims to enable computation- and communication-efficient GPU sharing across devices within local area networks (LANs), facilitating ubiquitous AI inference on heterogeneous personal devices. We achieve distributed task o…

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