Unverified50% confidenceFactExact time
In compression benchmarks, BF16 is usually used as the uncompressed baseline model to compare the performance loss after compression.
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Long-term
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11/5/2026
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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.70% 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.69% similarUnverified自回归推理的decode阶段是纯粹的内存带宽受限操作，将模型从FP16压缩到1-bit理论上可将token生成速度提升16倍66% 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 MoE66% similar
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