# Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy

DevFeed: [Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy](<https://devfeed.tech/articles/understanding-w8a8-int8-llm-quantization-half-the-size-better-performance-same-accuracy-12347.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/07/understanding-w8a8-int8-llm-quantization>)

Author: Sana Fayyaz

Published: 2026-09-07T07:01:34Z

Content type: article

Language: en

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

Topics: [quantization](<https://devfeed.tech/topics/quantization.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [llama](<https://devfeed.tech/topics/llama.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [compression](<https://devfeed.tech/tags/compression.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>)

## AI overview

This article explains INT8 W8A8 quantization for Llama 3.1 8B using SmoothQuant and GPTQ. It describes how quantization reduces memory requirements and can improve serving performance while preserving model accuracy, including a reported 46% reduction in model size and planned benchmark testing with vLLM and llm-compressor.

## Source excerpt

Large language models are expensive to serve. A model like Llama 3.1 8B in Bfloat16 (BF16) precision occupies roughly 15 GB of GPU memory. In BF16, each of the 8 billion parameters takes 2 bytes to store, which adds up to roughly 15 GB for the weights--and that's not all. The GPU needs memory for the key-value (KV) cache to store context for active requests, alongside intermediate tensor outputs (activations, as we call them) generated during inference. The post Understanding W8A8 INT8 LLM quantization: Half the size, better performance, same accuracy appeared first on Red Hat Developer.