--- title: A Practical Guide to LLM Model Naming Conventions description: When browsing Hugging Face, the same LLM often appears in many variants like FP16, INT8, Q4, GGUF, and GPTQ. These are not competing models but deployment... canonical: https://daily.dev/posts/a-practical-guide-to-llm-model-naming-conventions-ltnszweii twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A Practical Guide to LLM Model Naming Conventions | daily.dev og:description: When browsing Hugging Face, the same LLM often appears in many variants like FP16, INT8, Q4, GGUF, and GPTQ. These are not competing models but deployment... og:url: https://daily.dev/posts/a-practical-guide-to-llm-model-naming-conventions-ltnszweii og:image: https://api.daily.dev/og/posts/LTNszweIi.png og:image:alt: A Practical Guide to LLM Model Naming Conventions 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. # A Practical Guide to LLM Model Naming Conventions **[Callstack Blog](https://daily.dev/sources/callstack-blog)** · 4 min read · 1 upvotes · 0 comments ## Summary When browsing Hugging Face, the same LLM often appears in many variants like FP16, INT8, Q4, GGUF, and GPTQ. These are not competing models but deployment adaptations of the same base model for different hardware environments. Quantization reduces the bit-width used to store model weights, trading a small amount of numerical precision for lower memory usage and cost. FP16 suits high-VRAM GPUs, INT8 and 4-bit variants fit production GPU deployments, and GGUF formats enable CPU-only inference. The right quantization level depends entirely on your hardware constraints, latency targets, and cost requirements — not on which version is abstractly 'best'. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Quantizing LLMs Step-by-Step: Converting FP16 Models to GGUF](https://daily.dev/posts/quantizing-llms-step-by-step-converting-fp16-models-to-gguf-lqamqetgk) · Machine Learning Mastery · 2 upvotes · 0 comments - [LLM quantization guide: How to do it, and how it helps](https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok) · Red Hat Developer · 0 upvotes · 0 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: [#data-science](https://daily.dev/tags/data-science), [#llm](https://daily.dev/tags/llm), [#mlops](https://daily.dev/tags/mlops) [View this post on daily.dev](https://daily.dev/posts/a-practical-guide-to-llm-model-naming-conventions-ltnszweii) ```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":"A Practical Guide to LLM Model Naming Conventions","url":"https://daily.dev/posts/a-practical-guide-to-llm-model-naming-conventions-ltnszweii","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-practical-guide-to-llm-model-naming-conventions-ltnszweii"},"datePublished":"2026-03-22T11:30:51.636Z","dateModified":"2026-03-22T11:33:30.591Z","description":"When browsing Hugging Face, the same LLM often appears in many variants like FP16, INT8, Q4, GGUF, and GPTQ. 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