The decision involves balancing model size, required accuracy, data privacy needs, and network assumptions. On-device inference is fastest and works offline but limits model complexity. Cloud inference allows larger models but depends entirely on network quality. Often a hybrid approach is needed, requiring skilled mobile development partners to engineer this complexity effectively.
The decision involves balancing model size, required accuracy, data privacy needs, and network assumptions. On-device inference is fastest and works offline but limits model complexity. Cloud inference allows larger models but depends entirely on network quality. Often a hybrid approach is needed, requiring skilled mobile development partners to engineer this complexity effectively.
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The decision involves balancing model size, required accuracy, data privacy needs, and network assumptions. On-device inference is fastest and works offline but limits model complexity. Cloud inference allows larger models but depends entirely on network quality. Often a hybrid approach is needed, requiring skilled mobile development partners to engineer this complexity effectively.
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