---
id: aqtp
version: "0.9.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# aqtp — Accurate Quantized Training library.

License: permissive · Maintenance: active · Downloads: 457.8K/mo

## What it is and what it does

AQT is a quantization library for JAX that replaces standard tensor operations (dot_general, einsum, convolution) with quantized variants, enabling training of neural networks at reduced precision (int8, int4, or lower) without hand-tuning. It works by injecting quantized operations into JAX-based frameworks like Flax, Pax, and MaxText, and is designed to maintain bit-exact consistency between training and serving—avoiding the training-serving bias common in post-training quantization.

The library provides flexible configuration of forward and backward pass quantization separately, with support for various numerics (int8, int4, bfloat16, float8), calibration algorithms, and stochastic rounding. It is built on top of jax, jaxlib, flax, and absl-py, and is actively maintained by Google with demonstrated production use at scale and research validation across multiple papers and frameworks.

Use it for:

- Train large language models or vision models with int8 quantization on TPU or GPU clusters to reduce memory and accelerate computation.
- Conduct quantization research by configuring forward and backward bit widths independently and testing different calibration strategies.
- Deploy models trained with AQT without retraining or post-training quantization, since quantized weights are identical during training and serving.
- Integrate quantization into existing Flax models by replacing the dot_general operation without restructuring the model.
- Benchmark lower-bit training on production workloads to evaluate accuracy-efficiency tradeoffs.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

AQT provides quantization of tensor operations (matmul, einsum, conv) in JAX computations, enabling int8 and lower-bit model training with minimal configuration while maintaining training-serving consistency.

Yes, if you are training neural networks in JAX and need quantization. The library is actively maintained, has no known vulnerabilities, carries a permissive license, and is backed by Google's production use at scale. Install friction is low. The main constraint is a hard dependency on jax and jaxlib (which require compilation for your hardware), and the library is still in alpha (Development Status :: 3), so expect API changes. Best suited for researchers and practitioners already committed to the JAX ecosystem.

## Install

pip install aqtp
uv add aqtp
poetry add aqtp

## Installing aqtp

Before you install: Low install friction with a pure-Python wheel. Depends on four substantial packages (absl-py, jax, jaxlib, flax), all of which are actively maintained. The library itself shows active maintenance with a recent commit on 2026-08-06 and steady releases since 2022-01-13.

License in practice: Licensed under Apache Software License (permissive), imposing no restrictions on commercial or private use; attribution required but no copyleft obligations.

Quickstart:

pip install aqtp

import aqt.jax.v2.config as aqt_config

int8_config = aqt_config.fully_quantized(fwd_bits=8, bwd_bits=8)

Requires Python 3.10 or later; jaxlib has platform-specific compiled components (CPU, GPU, or TPU support depends on your JAX installation).

Verify before relying:

- Whether the package works with Python 3.12+ (classifiers list only 3.10 and 3.11)
- Performance gains on non-TPU hardware (description emphasizes TPU and contemporary ML accelerators)
- Compatibility with recent versions of Flax and Pax beyond what the description exemplifies

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 457.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

JAX tensor quantization, quantized training JAX, int8 model quantization, neural network quantization library, quantization-aware training, JAX quantized operations, low-bit training framework, quantization, jax, neural-networks

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