Unverified50% confidenceFactExact time
Pruning, quantization, and knowledge distillation belong to model-side compression, which changes the parameter representation of the deployed model, while input vectors still need to be transmitted and stored at original precision.
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10/19/2026
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UnverifiedMigrating a model from cloud inference to local operation typically requires compression steps such as quantization, pruning and distillation.77% similarUnverifiedExisting context compression approaches typically have the model itself perform the compression process, causing the timing, granularity, and information trade-offs of compression to depend on the model's real-time judgment, which both consumes inference resources and is difficult to audit.77% similarVerified模型压缩涵盖量化、剪枝、知识蒸馏三条主要技术路径75% similarVerifiedQuantization is a model compression technique that reduces model parameters from high-precision floating-point numbers to integer representations of lower bit-width75% similarUnverifiedSETAREH belongs to data-side compression: model parameters remain fixed, but each incoming input vector undergoes a structured transform and sparsification, reducing the dimension pairs that actually participate in inner-product computation.75% similar
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