: Optimization for specific GPU architectures (e.g., NVIDIA Ampere or Hopper). Conclusion
: Pre-defined sparsity levels (e.g., 1% outliers) to ensure predictable memory usage. SPQR.SPQRAlive.18.var
Large Language Models (LLMs) are often bottlenecked by memory requirements, limiting their deployment on consumer hardware. , introduced by researchers including Tim Dettmers and documented on arXiv , is a hybrid quantization technique. It achieves high-accuracy compression by isolating "outlier" weights that are sensitive to quantization and storing them in high precision, while compressing the remaining 99% of weights to 3-4 bits. 1. The Challenge of Quantization Error : Optimization for specific GPU architectures (e
: These sensitive weights (usually less than 1% of the total) are extracted and stored in their original 16-bit precision. limiting their deployment on consumer hardware.