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Quark Version List

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AMD Quark 0.10 documentation

Release Notes

Release Information

Getting Started with AMD Quark

Introduction to Quantization
Installation
Getting started: Introduction
Getting started: Quark for ONNX
Getting started: Quark for PyTorch
PyTorch Examples
ONNX Examples

Supported accelerators

AMD Ryzen AI
AMD Instinct
- Language Model Post Training Quantization (PTQ) Using Quark
  - FP4 Post Training Quantization (PTQ) for LLM models
  - FP8 Post Training Quantization (PTQ) for LLM models
- Evaluation of Quantized Models

Advanced AMD Quark Features for PyTorch

Configuring PyTorch Quantization for Large Language Models
Configuring PyTorch Quantization from Scratch
Save and Load Quantized Models
Exporting Quantized Models
Best Practices for Post-Training Quantization (PTQ)
Debugging quantization Degradation
Language Model Optimization
Activation/Weight Smoothing (SmoothQuant)
Auto SmoothQuant
Activation-aware Weight Quantization (AWQ)
- AWQ end-to-end demo
Block Floating Point 16
Extensions
- Integration with AMD Pytorch-light (APL)
- Brevitas Integration
Using MX (Microscaling)
Two Level Quantization Formats

Advanced Quark Features for ONNX

Configuring ONNX Quantization
Data and OP Types
Accelerate with GPUs
Mixed Precision
Block Floating Point 16 (BFP16)
BF16 Quantization
Microscaling (MX)
Microexponents (MX)
Accuracy Improvement Algorithms
Automatic Search for Model Quantization
Using ONNX Model Inference and Saving Input Data in NPY Format
Optional Utilities
Tools

Tutorials

AMD Quark Tutorial: PyTorch Quickstart
Quantizing a Diffusion Model using Quark
LLM Model Depth-Wise Pruning (beta)
Quantizing a Large Language Model with Quark

Third-party contributions

Introduction and guidelines

APIs

PyTorch APIs
ONNX APIs

Troubleshooting and Support

PyTorch FAQ
ONNX FAQ

AMD Quark release history
Quark license

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Last updated on Sep 26, 2025.

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