# PrismQuant

The work introduces PrismQuant, a quantizer-aware rotation framework that aligns the leading activation eigenspace with the group-constant subspace of asymmetric grouped INT4, formulates rotation design as a Ky Fan trace maximization with a provably optimal closed-form solution, realizes it with compact Householder (compact-WY) transforms without gradient training, and evaluates W4A4KV4 post-training quantization on Llama, Qwen, and Mistral (including dense models up to 70B and a 30B mixture-of-experts model): on Llama-3.2-3B it sets the state of the art among compared methods in perplexity and accuracy, on Llama-3.1-70B it attains 3.85 perplexity and 72.46% average zero-shot accuracy (0.22 percentage points below full precision), and in a Llama-3.

The work introduces PrismQuant, a quantizer-aware rotation framework that aligns the leading activation eigenspace with the group-constant subspace of asymmetric grouped INT4, formulates rotation design as a Ky Fan trace maximization with a provably optimal closed-form solution, realizes it with compact Householder (compact-WY) transforms without gradient training, and evaluates W4A4KV4 post-training quantization on Llama, Qwen, and Mistral (including dense models up to 70B and a 30B mixture-of-experts model): on Llama-3.2-3B it sets the state of the art among compared methods in perplexity and accuracy, on Llama-3.1-70B it attains 3.85 perplexity and 72.46% average zero-shot accuracy (0.22 percentage points below full precision), and in a Llama-3.