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Quark Version List

AMD Quark 0.11 documentation

Release Notes

Getting Started with AMD Quark

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

Advanced Quark Features for ONNX

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
FP8 Quantization with Per-Channel Static Weights and Per-Token Dynamic Activations
YOLO-X Tiny Quant example
Quark ONNX Quantization Tutorial For AdaQuant
Quark ONNX Quantization Tutorial For AdaRound
Quark ONNX Quantization Tutorial For Block Floating Point (BFP)
Quark ONNX Quantization Tutorial For Cross Layer Equalization (CLE)
Quark ONNX Quantization Tutorial For GPTQ
Quark ONNX Quantization Tutorial For Layerwise Percentile
Quark ONNX Quantization Tutorial For Mixed Precision
Quark ONNX Quantization Tutorial For Image Classification
Quark ONNX Quantization Tutorial For Smooth Quant
Quark ONNX Quantization Tutorial For Auto Search
Quark ONNX Quantization Tutorial For Image Classification
Quark ONNX Quantization Tutorial For AdaQuant
Quark ONNX Quantization Tutorial For Image Classification
Quark ONNX Quantization Tutorial For Auto Search
Quark ONNX Quantization Tutorial For Auto Search
Quark ONNX Quantization Tutorial For Auto Search
Quark ONNX Quantization Tutorial For Resnet50
Quark ONNX Quantization Tutorial For YOLOv8
Quantizing ONNX Models with Custom Operators Using Quark

Third-party contributions

Experimental Features

APIs

Troubleshooting and Support

Quantization Strategies

Quantization Strategies#

Last updated on Jan 16, 2026.