Skip to main content

Ctrl+K

Quark Version List

GitHub
Support

AMD Quark 0.9 documentation

Release Notes

Release Information

Getting Started with AMD Quark

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

Supported accelerators

AMD Ryzen AI
AMD Instinct
- FP8 (OCP fp8_e4m3) Quantization & Json_SafeTensors_Export with KV Cache
- Evaluation of Quantized Models

Advanced AMD Quark Features for PyTorch

Configuring PyTorch Quantization
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)
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

APIs

PyTorch APIs
ONNX APIs

Troubleshooting and Support

PyTorch FAQ
ONNX FAQ

AMD Quark release history
Quark license

AMD Quark APIs for ONNX
ONNX model...

ONNX model quantization

ONNX model quantization#

previous

AMD Quark APIs for ONNX

next

ONNX model optimization

Last updated on Jun 25, 2025.

Terms and Conditions
Quark Licenses and Disclaimers
Privacy
Trademarks
Statement on Forced Labor
Fair and Open Competition
UK Tax Strategy
Cookie Policy
Cookie Settings

© 2024 Advanced Micro Devices, Inc