--- title: 1 BIT Quantization, Is it lit or mid . | daily.dev description: An overview of 1-bit quantization for large language models, focusing on the BitNet architecture. Covers the basics of quantization (symmetric vs asymmetric),... canonical: https://daily.dev/posts/1-bit-quantization-is-it-lit-or-mid--t5wr6j15n twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: 1 BIT Quantization, Is it lit or mid . | daily.dev og:description: An overview of 1-bit quantization for large language models, focusing on the BitNet architecture. Covers the basics of quantization (symmetric vs asymmetric),... og:url: https://daily.dev/posts/1-bit-quantization-is-it-lit-or-mid--t5wr6j15n og:image: https://api.daily.dev/og/posts/t5wR6J15N.png og:image:alt: 1 BIT Quantization, Is it lit or mid . og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # 1 BIT Quantization, Is it lit or mid . **[Medium](https://daily.dev/sources/medium_js)** · 6 min read · 1 upvotes · 0 comments ## Summary An overview of 1-bit quantization for large language models, focusing on the BitNet architecture. Covers the basics of quantization (symmetric vs asymmetric), explains how binarization reduces model size and speeds up inference, and outlines BitNet's training techniques including straight-through estimators, mixed precision training, and large learning rates. References Microsoft research on 4-bit activation LLMs and concludes that BitNet achieves competitive perplexity with significantly lower memory and energy usage. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is BitNet and how does 1-bit quantization work for LLMs? BitNet is a transformer architecture that quantizes weights down to 1 bit, representing them as binary values (0 or 1) through a process called binarization. It uses a straight-through estimator to handle non-differentiable sign and clip functions during training, mixed precision training with high-precision gradients and optimizer states, and a large learning rate to converge faster than standard floating-point models, resulting in lower memory and energy use. _Explore more breakdowns of LLM quantization tradeoffs like this on daily.dev when evaluating efficient model architectures._ ### What is the difference between symmetric and asymmetric quantization? Symmetric quantization converts weights and activations from high-precision floats to lower-precision integers using a range evenly distributed around zero, and is simpler and more computationally efficient. Asymmetric quantization maps floating point numbers from a min-max range [β,α] into [0, 2^n-1] using the actual minimum and maximum tensor values, handling skewed distributions like activations better, though it is more complex. _Developers comparing quantization calibration methods can track these tradeoffs on daily.dev._ ## Similar posts on daily.dev - [What Happens When You Build an LLM Using Only 1s and 0s](https://daily.dev/posts/what-happens-when-you-build-an-llm-using-only-1s-and-0s-qoge390b0) · Towards Data Science · 0 upvotes · 0 comments - [Quantization from the ground up](https://daily.dev/posts/quantization-from-the-ground-up-h60w1pagu) · Hacker News · 3 upvotes · 1 comments - [5 LLM Quantization Techniques](https://daily.dev/posts/5-llm-quantization-techniques-nfunrpnuc) · Daily Dose of Data Science \| Avi Chawla \| Substack · 3 upvotes · 1 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm) [View this post on daily.dev](https://daily.dev/posts/1-bit-quantization-is-it-lit-or-mid--t5wr6j15n) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"1 BIT Quantization, Is it lit or mid .","url":"https://daily.dev/posts/1-bit-quantization-is-it-lit-or-mid--t5wr6j15n","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/1-bit-quantization-is-it-lit-or-mid--t5wr6j15n"},"datePublished":"2026-07-22T04:06:21.042Z","dateModified":"2026-09-14T06:37:31.785Z","description":"An overview of 1-bit quantization for large language models, focusing on the BitNet architecture. 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