mkvn/quantization-cache-amplification

Quantization as Cache Amplification Trillion-Parameter Mixture-of-Experts Inference on a Commodity Laptop Kavin Kumar, Neural Metrics 📄 Read the paper — 11 pages What this is Weight quantization is usually justified as footprint reduction. This work argues that for offloaded mixture-of-experts inference that framing misses the leverage. The binding resource is not storage capacity but the fraction of expert slots resident in DRAM — and storage traffic depends on… See the full description on the dataset page: https://huggingface.co/datasets/mkvn/quantization-cache-amplification.