--- title: LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU description: LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that keeps a pretrained LLM's weights frozen and instead trains two small low-rank... canonical: https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU | daily.dev og:description: LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that keeps a pretrained LLM's weights frozen and instead trains two small low-rank... og:url: https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu og:image: https://api.daily.dev/og/posts/0mYhkUbFU.png og:image:alt: LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU 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. # LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU **[Medium](https://daily.dev/sources/medium_js)** · 5 min read · 1 upvotes · 0 comments ## Summary LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that keeps a pretrained LLM's weights frozen and instead trains two small low-rank matrices whose product approximates the weight update. This reduces trainable parameters to under 1% of the original model, slashing GPU memory and checkpoint sizes while achieving performance close to full fine-tuning. The Hugging Face PEFT library makes implementation straightforward with key hyperparameters: rank (r), alpha scaling, and dropout. QLoRA extends this further by storing the frozen base model in 4-bit quantized form, enabling fine-tuning of billion-parameter models on consumer GPUs. LoRA adapters are typically applied to attention projection layers and can be saved and distributed independently from the base model. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is LoRA and how does it reduce the cost of fine-tuning large language models? LoRA (Low-Rank Adaptation) freezes the pretrained model's original weights and trains only small additional matrices that approximate the needed weight update. Instead of learning a full weight update matrix directly, it learns the product of two much smaller matrices, which for a 7B parameter model can reduce trainable parameters to less than 1% while keeping performance close to full fine-tuning. _Comparing fine-tuning approaches gets easier when daily.dev surfaces practical LoRA implementation details alongside the theory._ ### What is QLoRA and how does it differ from standard LoRA? QLoRA extends LoRA by storing the frozen base model in 4-bit quantized form instead of 16-bit precision, dramatically cutting memory usage while preserving strong performance. This combination of quantization and low-rank adapters lets developers fine-tune models with billions of parameters directly on consumer GPUs, rather than requiring enterprise-scale hardware. _Developers weighing memory-constrained fine-tuning options can track techniques like QLoRA through daily.dev._ ### Which layers of a transformer model are typically targeted when applying LoRA, and what hyperparameters control it? LoRA adapters are most commonly inserted into the attention projection layers, specifically the query, key, and value projections, though some implementations also apply them to feed-forward layers. Key hyperparameters include rank r (common values 4, 8, 16, 32, controlling adapter capacity), lora_alpha (a scaling factor for update strength), and dropout applied only to the adapters to reduce overfitting. _Configuring LoRA hyperparameters correctly matters, and daily.dev helps developers keep up with practical fine-tuning guidance._ ## Similar posts on daily.dev - [What is LoRA and how it lets anyone fine-tune a massive AI model on a single GPU](https://daily.dev/posts/what-is-lora-and-how-it-lets-anyone-fine-tune-a-massive-ai-model-on-a-single-gpu-vfwx1br5h) · Medium · 2 upvotes · 0 comments - [5 LLM Fine-tuning Techniques](https://daily.dev/posts/5-llm-fine-tuning-techniques-olu0mzhzh) · Daily Dose of Data Science \| Avi Chawla \| Substack · 3 upvotes · 0 comments - [Parameter-Efficient Fine-Tuning with LoRA Using Custom Data](https://daily.dev/posts/parameter-efficient-fine-tuning-with-lora-using-custom-data-m39vnbggw) · DigitalOcean Community · 1 upvotes · 0 comments - [\[2602.04118\] Learning to Reason in 13 Parameters](https://daily.dev/posts/2602-04118-learning-to-reason-in-13-parameters-fgsqvuufg) · Hacker News · 1 upvotes · 0 comments - [Fine-Tuned LLMs on Serverless Architecture](https://daily.dev/posts/fine-tuned-llms-on-serverless-architecture-x3h1laphl) · DigitalOcean Community · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#lora](https://daily.dev/tags/lora) [View this post on daily.dev](https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu) ```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":"LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU","url":"https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu"},"datePublished":"2026-08-04T11:46:05.402Z","dateModified":"2026-09-13T19:18:03.810Z","description":"LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that keeps a pretrained LLM's weights frozen and instead trains two small low-rank...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/fe36d8fa01d0ba2f3b926db43aa02e79?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/fe36d8fa01d0ba2f3b926db43aa02e79?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,deep-learning,lora","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"LoRA: Fine-Tuning Billion-Parameter LLMs on a Single GPU"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/lora-fine-tuning-billion-parameter-llms-on-a-single-gpu-0myhkubfu#faq","mainEntity":[{"@type":"Question","name":"What is LoRA and how does it reduce the cost of fine-tuning large language models?","acceptedAnswer":{"@type":"Answer","text":"LoRA (Low-Rank Adaptation) freezes the pretrained model's original weights and trains only small additional matrices that approximate the needed weight update. 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