# Quantized Models

This page is written from general quantization domain knowledge, not adapted from a source document - the local PyTorch reference checkout in this workspace covers pytorch.org's marketing/get-started site, not the `torch`/`torchao` API reference, so there is no local source to draw from for this page. Treat framework-specific claims as illustrative rather than confirmed against a specific PyTorch version.

A quantized tensor stores values at lower precision than the model was trained in - most commonly 8-bit or 4-bit integers instead of 16- or 32-bit floats - along with the small amount of extra metadata needed to recover an approximate real value from each stored integer. Opening a quantized checkpoint in Tensormorph works like any other, but several views and inspections behave differently once dtype metadata says a tensor is quantized rather than a plain float tensor.

## What's stored alongside the integers

A quantized tensor is never *just* integers - it carries the parameters needed to dequantize:

-   **Scale** - a float multiplier converting a stored integer back toward the original range.
-   **Zero-point** - an integer offset, for schemes that aren't symmetric around zero.
-   **Granularity** - whether scale/zero-point are shared per-tensor, per-channel (one pair per output channel - common for weight quantization), or per-group (a small run of adjacent values sharing one pair - common for low-bit weight-only quantization).

Tensormorph's [Scalar Inspector](/docs/editors/scalar-inspector) shows both the raw stored integer and the dequantized approximate value for any scalar you inspect, side by side, rather than only one or the other.

## Reading quantization in the Architecture and Matrix/Heatmap views

The **quantization boundaries** overlay (see [Architecture Editor](/docs/editors/architecture-editor)) draws a visible line wherever dtype or quantization scheme changes across a module - useful for spotting, at a glance, which layers a quantization pass actually touched versus left at full precision. In the Matrix/Heatmap view, a dedicated quantization shading mode colors each element by its quantization granularity group rather than by value, making per-channel or per-group boundaries visible directly on the tensor itself.

## Quantization diff

Comparing a quantized model against its full-precision source is a distinct operation from an ordinary [weight diff](/docs/compare-and-morph/weight-diff), because the "expected" difference is the quantization error itself, not a meaningful change - see [Quantization Diff](/docs/compare-and-morph/quantization-diff). The interesting signal is where the *error* is unusually large relative to a tensor's own scale, which is where quantization silently degraded something rather than merely rounding it.

## Related resources

-   [Dtype and Quantization](/docs/core-concepts/dtype-and-quantization) - the underlying dtype model quantized tensors extend.
-   [Quantization Diff](/docs/compare-and-morph/quantization-diff) - comparing a quantized model against its full-precision source.
-   [GPU Backend Compatibility](/docs/reference/gpu-backend-compatibility) - low-bit quantized kernels are not uniformly supported across every compute backend.