Overview of natively supported quantization schemes in 🤗 Transformers

Memory-efficient Diffusion Transformers with Quanto and Diffusers

A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes

KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers

arXiv:2607. 21446v1 Announce Type: new Abstract: Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-bit formats cannot represent.

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization

arXiv:2512. 00956v3 Announce Type: replace Abstract: Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization errors.

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

Exploring Quantization Backends in Diffusers