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AMD Quark 0.12 documentation

Release Notes

Release Information

Getting Started with AMD Quark

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

Supported accelerators

AMD Ryzen AI
AMD Instinct

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
File-to-File LLM Quantization
Language Model Optimization
Activation/Weight Smoothing (SmoothQuant)
Auto SmoothQuant
Activation-aware Weight Quantization (AWQ)
- AWQ end-to-end demo
SVD-Based Error Correction (SVDQuant)
Block Floating Point 16
Extensions
- Integration with AMD Pytorch-light (APL)
- Brevitas Integration
Using MX (Microscaling)
Two Level Quantization Formats
Using Quark Agent Skills (Claude Code)
Using Quark Agent Skills (Claude Code)

Advanced Quark Features for ONNX

Configuring ONNX Quantization
Data and Op Types
Accuracy Improvement
Efficiency Improvement
Tooling

Third-party contributions

Introduction and guidelines

Experimental Features

Quark CLI
- Shapeshifter
  - ONNX Model Passes
  - PyTorch Model Passes
Mix Precision Auto-Search
Blockwise Joint Tuning
Built-in Profiling
ONNX Examples

APIs

PyTorch APIs
ONNX APIs
- Quantization
- Quantizer Configuration

Troubleshooting and Support

PyTorch Troubleshooting
ONNX Troubleshooting

AMD Quark release history
Quark license

AMD Quark APIs for ONNX
Quantization configuration
Quantization Strategies

Quantization Strategies

Quantization Strategies#

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Quantization configuration

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Data Types

Last updated on Jul 03, 2026.

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