qint

Qint is a term used in computing to denote a quantized integer data type, typically used to represent fixed‑point numbers with reduced precision to save memory and improve computational efficiency. It is not a single standardized type; rather, qints are implemented in multiple frameworks as 8‑, 16‑, or sometimes smaller signed or unsigned integers, used to encode real-valued quantities after a quantization step.

Quantized integers are usually accompanied by quantization parameters, most commonly a scale and a zero‑point. A real value is recovered approximately by multiplying the integer by the scale and adjusting by the zero‑point, or, in some representations, through a symmetric mapping. Quantization can be applied per-tensor or per-channel to preserve accuracy across different weights or activations.

In practice, qints are widely used to compress weights and activations in neural networks, enabling smaller model sizes and faster inference on resource‑constrained devices. Arithmetic is often carried out in higher precision (for example 32‑bit accumulators) during computations and then re‑quantized to the target qint format to maintain efficiency.

Implementations vary by framework. PyTorch, for example, exposes qint8 and qint32 dtypes as part of its quantization APIs; TensorFlow Lite supports quantized tensors with qint8 and related formats. The lack of a universal standard means the exact bit width, range, and APIs can differ between ecosystems, even as the underlying idea of quantized integers remains common.

See also: fixed‑point arithmetic, neural network quantization, int8, int16, int32, tensor quantization.