Edge devices typically have limited resources, such as computing power, storage space, and energy supply, which restricts their capability to handle complex tasks or large sets of data.
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As business demands grow, edge computing systems need to be easily scalable and maintainable to accommodate evolving workloads.
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The data processed in edge inference may contain sensitive information, necessitating the assurance of data security and privacy protection.
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Network connections in edge environments must be stable and reliable to ensure continuous data transmission and accurate inference results.
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Application scenarios such as autonomous driving and quantitative trading require real-time data analysis and decision-making, where any delay could lead to serious consequences.
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Large language model inference typically requires significant computing resources, resulting in unacceptable latency when processed in traditional centralized data centers.
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