Practical overview
When it fits
Use PPQ during model optimization and deployment preparation when you need an offline quantization workflow and want control over calibration, optimization passes or target-platform behavior.
Who it is for
- Machine-learning engineers preparing trained models for deployment on constrained hardware.
- Developers who need to calibrate, analyze or fine-tune quantized models.
- Engineers extending quantization or operator behavior for a model or inference platform.
Documented uses
- Convert floating-point model computations to lower-bit representations.
- Calibrate quantization parameters and analyze quantization error.
- Configure quantization by operator or tensor, including bit width and granularity.
- Simulate quantized execution and prepare model output for a target inference framework.
Requirements & limits
The public GitHub repository lists an Apache-2.0 license and provides source, Docker and pip installation paths.
- The documented installation requires CUDA and compiler prerequisites; Windows has additional specified setup requirements.
- PPQ includes its own ONNX execution logic and describes inference and quantization without ONNX Runtime.
- The project separates quantization and simulation from the inference frameworks used for deployment, and documents quantizers and export logic for multiple targets.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install PPQ from the source repository
Prepare a local PPQ installation using the README's source-installation sequence.
Before you start
- CUDA Toolkit
- A compiler; Windows instructions specify Ninja, Visual Studio 2019 and a C++ compiler on PATH
- On Windows, PyTorch 1.10 or later
Steps
Install CUDA Toolkit.
Install the compiler prerequisites for your operating system.
Clone the PPQ repository and change into its directory.
Install the requirements with pip, then run the setup.py installation command.
Expected outcome
PPQ is installed from the cloned source according to the repository's documented procedure.
Guide limitations
- The installation instructions do not establish that every model, platform or target inference framework will work with a given setup.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Ppq | Convert floating-point model computations to lower-bit representations. | Linux, macOS, Windows | The public GitHub repository lists an Apache-2.0 license and provides source, Docker and pip installation paths. |
| Hyperpose | Customize pose-estimation architectures and datasets. | Docker, Linux, macOS, Windows | |
| Multi Task Nlp | Define multiple tasks and their data and model settings in a YAML file. | Linux, macOS, Windows | The project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- OpenPPL/ppq · Official homepage title · retained in its source language