Unverified50% confidenceFactExact time
The maturation of quantization compression (INT4/INT8), sparsification, and efficient small-parameter architectures has made models in the 1B–10B range practically useful.
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Long-term
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11/6/2026
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UnverifiedOn-device large models compress parameter counts to the 1B–7B level through techniques such as model quantization (compressing FP32 to INT4/INT8), knowledge distillation, and structural pruning, enabling real-time inference on local chips.74% similarVerifiedINT8 quantization can compress model size to 1/2 to 1/4 of the original.74% similarVerifiedModel quantization is the compression of model weights and activation values from higher bit widths such as FP16/BF16 to FP8, INT8, or even INT4, in order to reduce memory usage, improve memory access bandwidth utilization, and accelerate computation.72% similarVerifiedQuantization compresses the storage precision of model parameters from 16-bit down to 8-bit or even 4-bit, thereby reducing memory usage, but at the cost of potential loss in model accuracy.71% similarUnverified2-bit quantization represents each parameter with 2 binary bits, achieving an 8x compression ratio compared to FP16, making it the highest compression tier among currently practical quantization methods, and it is generally only suitable for large-parameter models with redundant structures such as MoE71% similar
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