04GPTQ and AWQ both produce INT4 weights with group scales. What does each one optimize, and when does one beat the other?▼mediumNewNVIDIARed HatMetaunlockedThe two methods ship identical file formats and run on identical kernels, so the difference is entirely in how the rounding was chosen. The answer shows the one design choice that makes one of them fragile when traffic stops looking like the calibration set.Open full answer →
14A quantized MoE holds its benchmark scores but loses accuracy on rare domains, traced to rarely routed experts. Explain why and fix it.▼expertNewMistral AITogether AIFireworks AI◆ premiumCalibration data reaches each expert only through the router, so the experts that matter for rare domains see almost none of it. The answer counts the tokens, shows the point where a Hessian-based quantizer becomes worse than plain rounding, and builds a recipe that does not depend on luck.Open full answer →