The 70B Precision Trade-off: Weights vs. Intelligence
Every parameter in a neural network is stored as a floating-point number. A 2025 deep dive by Meta Intelligence on model quantization highlights that converting FP16 weights to INT4 immediately reduces memory by 75%, typically keeping accuracy loss under 1%. When you scale down to lower bit-widths, the primary concern is preventing information collapse. Sophisticated … Read more