Spqr.spqralive.18.var

: It enables models like LLaMA-65B to fit on a single 24GB or 32GB GPU while maintaining performance.

Based on experimental data from the SpQR GitHub Repository , the method offers: SPQR.SPQRAlive.18.var

: It uses a Hessian-based regularizer to identify which weights are most sensitive to quantization. : It enables models like LLaMA-65B to fit

: Optimization for specific GPU architectures (e.g., NVIDIA Ampere or Hopper). Conclusion SPQR.SPQRAlive.18.var

: The remaining "non-sensitive" weights are quantized to a low bit-width (e.g., 3 or 4 bits) using a very small group size to minimize local error.