Unverified50% confidenceFactExact time
The fundamental reason for the response quality gap between on-device and cloud models lies in parameter scale and quantization compression; on-device models undergo aggressive quantization (such as INT4/INT8) and distillation, resulting in degraded instruction-following capability.
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50%
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Long-term
Relevance
9/16/2026
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UnverifiedRelying on cloud-based APIs means losing full control over the underlying model behavior, and guardrail performance may fluctuate after a vendor updates the model.77% similarUnverifiedOn-device large model parameter counts are typically between 1B and 13B, and there is a significant gap compared to cloud-based large models in complex reasoning and long-document understanding76% similarUnverified本地模型与云端模型之间的能力差距正在快速缩小74% similarUnverified模型性能的瓶颈不仅在于参数量，更在于表示的质量，嵌入空间坍缩的模型即使参数再多也无法充分发挥潜力74% similarUnverifiedINT8 or INT4 quantization schemes can further compress model size, but introduce a certain degree of generation quality loss.71% similar
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