# Understanding W8A8 INT8 LLM quantization: Accuracy and performance results

DevFeed: [Understanding W8A8 INT8 LLM quantization: Accuracy and performance results](<https://devfeed.tech/articles/understanding-w8a8-int8-llm-quantization-accuracy-and-performance-results-17433.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/14/understanding-w8a8-int8-llm-quantization-accuracy-and-performance-results>)

Author: Sana Fayyaz

Published: 2026-09-14T13:01:43Z

Content type: article

Language: en

Sources: [Red Hat](<https://devfeed.tech/sources/red-hat.md>), [Red Hat Developer](<https://devfeed.tech/sources/red-hat-developer.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compression](<https://devfeed.tech/tags/compression.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

## AI overview

The article evaluates W8A8 INT8 quantization of a Llama 3.1 8B Instruct model. It describes reducing the model from 14.9 GB to 8.0 GB with SmoothQuant and GPTQ, then compares the base and compressed models on four benchmarks to assess accuracy and performance.

## Source excerpt

In Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy, we compressed a Llama 3.1 8B Instruct model from 14.9 GB to 8.0 GB using 8-bit integer (INT8) W8A8 quantization with SmoothQuant and Generative Pre-trained Transformer Quantization (GPTQ). The post Understanding W8A8 INT8 LLM quantization: Accuracy and performance results appeared first on Red Hat Developer.