[{"id":"5edd9c44-2cc9-4874-a568-373674fbf789","thread_id":"e2385bc9-7ca3-46c7-a32f-6c6aad28052f","created_at":"2025-11-21T00:16:25Z","state":"done","prompt":"Any 6gb LLM's for text generation?","reply":"<details><summary>Searched with Kagi<p></p>\n<ul>\n<li>6GB LLM text generation models</li></ul></summary><br>\nSources (10):<br>\n<strong>Best current model that can run entirely on 6 GB VRAM? (No GPU ...</strong><br>\n<a href=\"https://redlib.service.silvenga.com/r/LocalLLaMA/comments/1fp6ihr/best_current_model_that_can_run_entirely_on_6_gb/\" target=\"_blank\">https://redlib.service.silvenga.com/r/LocalLLaMA/comments/1fp6ihr/best_current_model_that_can_run_entirely_on_6_gb/</a><br>\nSep 25, 2024 Just wondering what the best and quickest model is currently for running a model with only 6 GB of VRAM. Right now im using Gemma 2 9B and works well for ...\n\n<p><strong>Top 10 Smallest LLM to Run Locally - ML Journey</strong><br>\n<a href=\"https://mljourney.com/top-10-smallest-llm-to-run-locally/\" target=\"_blank\">https://mljourney.com/top-10-smallest-llm-to-run-locally/</a><br>\n Large Language Models (LLMs) have become essential for natural language processing (NLP) applications such as chatbots, text generation, and code completion. While powerful, many of these models require high-end GPUs or cloud computing resources, making them difficult to run on local devices. However, advancements in AI have led to the development of smaller LLMs optimized for local use.</p>\n<p><strong>Enabling On-Device Large Language Model Personalization with...</strong><br>\n<a href=\"https://arxiv.org/pdf/2311.12275\" target=\"_blank\">https://arxiv.org/pdf/2311.12275</a><br>\nNov 22, 2023 After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated con-versation data to generate user-specific and personalized responses in real-time.We propose to use the LLM model to generate additional data that are semantically similar to the selected data to enhance fine-tuning quality. As this is the first work for on-device LLM personalization, no state-of-the-art is available, and we constructed a few vanilla base-lines for comparison.</p>\n<p><strong>Suggestions for low spec PC / GPUs ? #1704 - GitHub</strong><br>\n<a href=\"https://github.com/oobabooga/text-generation-webui/discussions/1704\" target=\"_blank\">https://github.com/oobabooga/text-generation-webui/discussions/1704</a><br>\nMay 02, 2023 Suggestions for low spec PC / GPUs ?GPTQ 4bit quantized models should consume less resources, so those are something you might want to look for; with the parameter --pre_layer you may be able to fit some of them into the 6 GB VRAM. I really like Vicuna-13B-GPTQ, she and her context somewhat fit into 12 GB with --pre_layer 32. You may have some luck with Vicuna-7B-GPTQ, from what I understand ...</p>\n<p>**<a href=\"https://arxiv.org/abs/2405.06652\" target=\"_blank\">https://arxiv.org/abs/2405.06652</a>ps://arxiv.org/abs/2405.06652](https://arxiv.org/abs/2405.06652)<br>\nMay 14, 2024 In this paper, a tool for detecting LLM AI text generation is developed based on the Transformer model, aiming to improve the accuracy of AI text generation detection and provide reference for subsequent research.View a PDF of the paper titled Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm, by Yuhong Mo and 4 other authors.</p>\n<p><strong>GitHub - rmusser01/tldw_chatbook: Standalone Client</strong><br>\n<a href=\"https://github.com/rmusser01/tldw_chatbook\" target=\"_blank\">https://github.com/rmusser01/tldw_chatbook</a><br>\nOct 07, 2025 ... built with the Textual framework for interacting with various Large Language Model ... It provides a complete ecosystem for AI-powered interactions including conversation management, character/persona chat, notes with bidirectional file sync, media ingestion, advanced RAG (Retrieval-Augmented Generation) capabilities, comprehensive LLM evaluation system, and optional web browser access.</p>\n<p><strong>GitHub - ray-project/llm-numbers: Numbers every LLM</strong><br>\n<a href=\"https://github.com/ray-project/llm-numbers\" target=\"_blank\">https://github.com/ray-project/llm-numbers</a><br>\nAug 23, 2025 What this means is that for many practical applications, it’s much better to use GPT-4 for things like generating high quality fine tuning data, or for automated evaluation of other models -- things you might only do once instead of it living in the middle of your inference cycle. ... GPT-4 charges differently for the prompt and the generated output) – so you really need to check on ...</p>\n<p><strong>️ Local Multimodal Image Generation with Ollama & Stable ... - GitHub</strong><br>\n<a href=\"https://github.com/ImdataScientistSachin/Local-Multimodal-Image-Generation-with-Ollama-Stable-Diffusion\" target=\"_blank\">https://github.com/ImdataScientistSachin/Local-Multimodal-Image-Generation-with-Ollama-Stable-Diffusion</a><br>\n A fully local and private pipeline for high-quality image synthesis. This project seamlessly integrates a local Large Language Model (LLM) running on Ollama for automated, intelligent prompt engineering with the Stable Diffusion WebUI (AUTOMATIC1111) API for image generation and dynamic model checkpoint switching.</p>\n<p><strong>Personalization of Large Language Models: A Survey</strong><br>\n<a href=\"https://arxiv.org/pdf/2411.00027\" target=\"_blank\">https://arxiv.org/pdf/2411.00027</a><br>\nNov 04, 2024 Subsequently, another transformation function, the personalized prompt generation function ϕp, combines the original input x and the output of the adaptation function to form a personalized input x¯. Ultimately, the personalized text yˆ generated by an LLM M isThis embedding guides a fixed LLM to generate personalized outputs without modifying the model’s parameters, significantly improving personalization performance over retrieval-based methods. 5.4 Personalization via Reinforcement Learning from Human Feedback (RLHF).</p>\n<p><strong>GitHub - deepseek-ai/DeepSeek-LLM: DeepSeek LLM: Let there be...</strong><br>\n<a href=\"https://github.com/deepseek-ai/DeepSeek-LLM\" target=\"_blank\">https://github.com/deepseek-ai/DeepSeek-LLM</a><br>\nNov 29, 2023 1. Introduction. Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.</p>\n</details><p>While there are no widely known large language models (LLMs) that are exactly 6 GB in size, several models can effectively run within 6 GB of VRAM—especially when quantized (i.e., compressed for efficiency). These models are suitable for text generation on consumer-grade hardware.</p>\n<p>Based on current information:</p>\n<ul>\n<li>\n<p><strong>Gemma 2 9B</strong> (9 billion parameters) can run on 6 GB of VRAM, as noted in a Reddit discussion from September 2024 <citation>(1)</citation>. Using 4-bit or 5-bit quantization techniques (such as GGUF or GPTQ), the model’s memory footprint is significantly reduced, enabling it to fit within 6 GB of GPU memory.</p>\n</li>\n<li>\n<p><strong>Vicuna-7B</strong> (7 billion parameters) is another strong candidate. With 4-bit GPTQ quantization, it can fit into 6–8 GB of VRAM, making it feasible for systems with 6 GB of VRAM using tools like <code>--pre_layer</code> to offload some layers to CPU <citation>(4)</citation>.</p>\n</li>\n<li>\n<p>Models like <strong>Llama 2 7B</strong> and <strong>Mistral 7B</strong> are also commonly used in quantized versions (e.g., 4-bit GGUF) and can run efficiently on 6 GB VRAM setups, especially when running inference via CPU + GPU hybrid modes using frameworks like llama.cpp or Ollama <citation>(8)</citation>.</p>\n</li>\n</ul>\n<p>In summary, while no standard LLM is precisely \"6GB\" in size, <strong>7B–9B parameter models with quantization</strong> (e.g., Gemma 2 9B, Vicuna-7B, Mistral 7B) are practical choices for text generation on systems with 6 GB of VRAM <citation>(1,4)</citation>. These models balance performance and resource usage effectively for local deployment.</p>","md":"While there are no widely known large language models (LLMs) that are exactly 6 GB in size, several models can effectively run within 6 GB of VRAM—especially when quantized (i.e., compressed for efficiency). These models are suitable for text generation on consumer-grade hardware.\n\nBased on current information:\n\n- **Gemma 2 9B** (9 billion parameters) can run on 6 GB of VRAM, as noted in a Reddit discussion from September 2024 <citation>(1)</citation>. Using 4-bit or 5-bit quantization techniques (such as GGUF or GPTQ), the model’s memory footprint is significantly reduced, enabling it to fit within 6 GB of GPU memory.\n\n- **Vicuna-7B** (7 billion parameters) is another strong candidate. With 4-bit GPTQ quantization, it can fit into 6–8 GB of VRAM, making it feasible for systems with 6 GB of VRAM using tools like `--pre_layer` to offload some layers to CPU <citation>(4)</citation>.\n\n- Models like **Llama 2 7B** and **Mistral 7B** are also commonly used in quantized versions (e.g., 4-bit GGUF) and can run efficiently on 6 GB VRAM setups, especially when running inference via CPU + GPU hybrid modes using frameworks like llama.cpp or Ollama <citation>(8)</citation>.\n\nIn summary, while no standard LLM is precisely \"6GB\" in size, **7B–9B parameter models with quantization** (e.g., Gemma 2 9B, Vicuna-7B, Mistral 7B) are practical choices for text generation on systems with 6 GB of VRAM <citation>(1,4)</citation>. These models balance performance and resource usage effectively for local deployment.","profile":{"id":null,"name":null,"subtitle":"Fast, straight answer (<5 secs)","model":"ki_quick","model_name":"Quick","model_provider":"kagi","model_input_limit":1000000,"recommended":true,"model_info":"\n\n<div class=\"heading\">\n <div class=\"left\">\n \n \n \n <svg class=\"model-icon icon-xs\" data-model=\"ki_quick\">\n <use href=\"#icon-kagi\"/>\n </svg>\n <h3>\n \n <span class=\"model-provider\">Kagi</span>\n \n <span class=\"model-name\">none</span>\n </h3>\n </div>\n\n \n <a href=\"/settings?p=custom_assistant&id=none\">\n <svg class=\"icon-sm edit-icon\" aria-hidden=\"true\">\n <use href=\"#icon-pencil\" />\n </svg>\n </a>\n \n</div>\n\n\n<hr />\n<ul>\n \n <li>\n <h4>\n Cost\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Cheap\">\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --low\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --low\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --low\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --low\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --low\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Quality\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 2 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Speed\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 5 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Privacy\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 5 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li class=\"divider\" aria-hidden=\"true\"></li>\n <li>\n <h4>Last updated</h4>\n <span class=\"date\">\n \n \n 2025-11-18\n \n \n </span>\n </li>\n \n</ul>\n","model_provider_name":"Kagi","internet_access":true,"personalizations":true,"shortcut":null,"is_default_profile":false},"metadata":"<li>\n <span class=\"attribute\">Model</span>\n <span class=\"value\"><i class=\"icon-xs mr-2\"><svg><use href=\"#icon-kagi\"></use></svg></i>Quick</span>\n</li>\n<li>\n <span class=\"attribute\">Version</span>\n <span class=\"value\">ki_quick-2025-11-18</span>\n</li>\n<li>\n <span class=\"attribute\">Speed (tok/s)</span>\n <span class=\"value\">385</span>\n</li>\n<li>\n <span class=\"attribute\">Tokens</span>\n <span class=\"value\">4,201</span>\n</li>\n<li>\n <span class=\"attribute\">Cost / Total ($)</span>\n <span class=\"value\">0.007 / 0.007</span>\n</li>\n<li>\n <span class=\"attribute\">End to end time (s)</span>\n <span class=\"value\">2.27</span>\n</li>\n\n","documents":[]}]