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AMD Quark 0.11.1 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

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
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
Automatic Search Pro for Model Quantization
Latency and Memory profiling
Using ONNX Model Inference and Saving Input Data in NPY Format
Optional Utilities
Tools

Tutorials

Quark for PyTorch
Quark for ONNX

Third-party contributions

Introduction and guidelines

Experimental Features

Quark CLI
- ONNX Adapter

APIs

PyTorch APIs
ONNX APIs
- Quantization
- Quantizer Configuration

Troubleshooting and Support

PyTorch Troubleshooting
ONNX Troubleshooting

AMD Quark release history
Quark license

Extensions for PyTorch

Extensions for PyTorch

Extensions for PyTorch#

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BFP16 (Block floating point) Quantization

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Using OCP MX (Microscaling)

Last updated on Feb 19, 2026.

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