AI inference glossary
Numerical precision

INT8

Also known as 8-bit integer quantization, W8A8

In plain English

INT8 stores numbers as 8 bit integers with a scale factor, halving memory versus 16 bit formats and doubling math rates on supporting hardware.

Technical definition

INT8 is an 8 bit integer numerical format used with per tensor or per channel scale factors to represent model weights and activations in quantized inference.

Engineering details

Integer quantization maps floating point values onto 256 evenly spaced levels via a scale, and sometimes a zero point. Uniform spacing handles outliers poorly, so techniques such as SmoothQuant migrate activation outliers into weights before quantizing both sides, in the W8A8 pattern. On older accelerator generations INT8 was the primary fast path below 16 bits, while newer chips add FP8, whose exponent gives it a wider dynamic range at the same bit width.

Why it matters

INT8 defined the first mainstream wave of LLM quantization and remains relevant on hardware without floating point 8 bit support. The INT8 versus FP8 contrast also illustrates the core quantization tradeoff between uniform precision and dynamic range.

How to read it in InferenceX

InferenceX labels every result with its precision, and its comparison families exist because format changes moved curves so much. Modern recipes on Blackwell and MI350 class hardware favor FP8 and FP4 paths, with integer formats appearing in specific weight quantized configurations.