Unverified50% confidenceFactExact time
On-device models refer to small models whose parameter scale is compressed enough to allow real-time inference on phone chips, typically using quantization, pruning and distillation to reduce compute and memory usage.
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50%
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Long-term
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10/17/2026
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UnverifiedOn-device inference context length should be determined by the device's available memory, not by the model's theoretical maximum.75% similarVerifiedThe essence of model quantization is to map a model's floating-point weights and activations (typically FP32) to low-bit-width representations (such as INT8 or INT4), thereby reducing memory usage, improving inference speed, and lowering power consumption.75% similarUnverifiedKnowledge distillation, quantization, and pruning are key model compression techniques for achieving real-time inference on mobile devices.75% similarUnverifiedInference frameworks enable models to execute in real time on consumer-grade devices through optimization techniques such as quantization (compressing FP32 weights to INT8 or even INT4) and operator fusion.75% similarVerified模型蒸馏是将大模型的能力压缩到小模型中以降低推理成本的技术74% similar
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