Unverified50% confidenceFactExact time
Model quantization and knowledge distillation technologies enable small language models with parameter counts in the hundreds of millions to run on memory-constrained mobile devices.
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50%
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Long-term
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10/6/2026
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UnverifiedThanks to the unified memory architecture, large language models with parameter counts at the 70-billion level can theoretically be fully loaded into memory for local inference.76% similarVerifiedKnowledge distillation technology enables small models to extract a considerable proportion of capability from large models, undermining the validity of fixed compute thresholds as a proxy metric for capability.75% similarUnverifiedLetting the model judge which information is worth memorizing saves more storage and context costs than mechanically saving all conversations, and improves the signal-to-noise ratio of memory.74% similarUnverified配合模型量化技术，7B甚至13B参数级别的模型能够在手机上以每秒数个token的速度进行推理73% similarUnverifiedOne of the core bottlenecks for running large language models locally is memory capacity, as model parameters need to be loaded into RAM or VRAM.73% similar
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