---
title: Quantization, parallelism, and trade-offs
seo_title: Quantization, parallelism, and trade-offs — video
description: Quantization reduces bytes per parameter; tensor and pipeline parallelism distribute compute and memory. Each choice moves memory, quality, communication, and latency.
keywords: LLM quantization, tensor parallelism, pipeline parallelism, expert parallelism, context parallelism, FP8, INT4, AWQ, GPTQ
date: '2026-09-12T00:00:00+00:00'
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    <h1>Quantization, parallelism, and trade-offs</h1><p>Quantization reduces bytes per parameter; tensor and pipeline parallelism distribute compute and memory. Each choice moves memory, quality, communication, and latency.</p>
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    <div class="s5-video-watch__snippet-grid"><article><span>01</span><h2>Quantization does not mean that the whole model has one precision</h2><p>Common notation already carries important scope information.</p></article>
<article><span>02</span><h2>The basic operation introduces representation error</h2><p>For a simple symmetric quantizer, we can write:</p></article>
<article><span>03</span><h2>Weight-only and weight-plus-activation quantization target different bottlenecks</h2></article></div>
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<li><a href="?t=12" data-s5-video-seek="12"><time>0:12</time><span>Parallelism distributes the model across devices</span></a></li>
<li><a href="?t=24" data-s5-video-seek="24"><time>0:24</time><span>Optimizing one metric moves others</span></a></li></ol></section>
  
  <aside class="s5-video-watch__source"><div><span class="s5-eyebrow">Context and evidence</span><h2>Continue with the full article</h2><p>The chapter develops the mechanism, primary sources, limitations and connections to the rest of the series.</p></div><a class="s5-video-watch__source-link" href="https://5sigmas.com/en/series/llm-inference-engineering-economics/03-quantization-parallelism-memory-quality-tradeoffs/">Read the article →</a></aside>
  <section class="s5-video-watch__related" aria-labelledby="related-videos-title"><div class="s5-video-watch__section-head"><span class="s5-eyebrow">Next step</span><h2 id="related-videos-title">Related videos</h2></div><div class="s5-video-watch__related-grid"><article><a href="https://5sigmas.com/en/videos/series/evaluating-ai-systems-production/06-observability-failure-taxonomies-production-eval-repair-feedback-loops/"><img src="https://5sigmas.com/en/series/evaluating-ai-systems-production/06-observability-failure-taxonomies-production-eval-repair-feedback-loops.jpg" alt="" loading="lazy" width="1280" height="720"><span>Other topics · 0:36</span><strong>Observability, failure taxonomies, and feedback loops</strong></a></article>
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