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JustQuant introduces a method for 4-bit activation quantization in large models that avoids the need for smoothing, singular value decomposition, or rotation. This approach addresses the challenges of activation quantization more efficiently than prior PTQ and QAT methods.
JustQuant introduces a method for 4-bit activation quantization in large models that avoids the need for smoothing, singular value decomposition, or rotation. This approach addresses the challenges of activation quantization more efficiently than prior PTQ and QAT methods.
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Recent generative models have become increasingly powerful, but their inference cost continues to grow. Model quantization offers a promising way to compress these models and accelerate inference. However, at 4 bits, activation quantization is substantially more challenging than weight quantization. Recent...
Reducing the bit-width of activations to 4 bits can significantly compress models and speed up inference, which is critical as generative models grow larger and more costly to run. JustQuant's technique simplifies the quantization process, potentially making low-bit inference more practical.
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