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

Quanto: a PyTorch quantization backend for Optimum

On the Expressive Power of Transformers

arXiv:2608. 12671v1 Announce Type: new Abstract: Multi-layer transformers form the critical component of essentially all large language models (LLMs) in use today.

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

VQ-bench: A Composable Vector Quantization Framework

arXiv:2608. 11240v1 Announce Type: new Abstract: Vector quantization is an old problem but has recently become central to AI infrastructure.

FPTQuant: Function-Preserving Transforms for LLM Quantization

arXiv:2506. 04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time.

Introducing Optimum: The Optimization Toolkit for Transformers at Scale