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A Reddit thread discussing preferred local Large Language Model (LLM) setups for tasks like summarizing text, coding, and general use. Users share their model choices (Gemma, Qwen, Phi, etc.) and frameworks (llama.cpp, Ollama, EXUI) along with potential issues and configurations.
Model | Use Cases | Size (Parameters) | Approx. VRAM (Q4 Quantization) | Approx. RAM (Q4) | Notes/Requirements |
---|---|---|---|---|---|
Gemma 3 (Meta) | Summarization, conversational tasks, image recognition, translation, simple writing | 3B, 4B, 7B, 8B, 12B, 27B+ | 2-4GB (3B), 4-6GB (7B), 8-12GB (12B) | 4-8GB (3B), 8-12GB (7B), 16-24GB (12B) | Excellent performance for its size. Recent versions have had memory leak issues (see Reddit post – use Ollama 0.6.6 or later, but even that may not be fully fixed). QAT versions are highly recommended. |
Qwen 2.5 (Alibaba) | Summarization, coding, reasoning, decision-making, technical material processing | 3.5B, 7B, 72B | 2-3GB (3.5B), 4-6GB (7B), 26-30GB (72B) | 4-6GB (3.5B), 8-12GB (7B), 50-60GB (72B) | Qwen models are known for strong performance. Coder versions specifically tuned for code generation. |
Qwen3 (Alibaba - upcoming) | General purpose, likely similar to Qwen 2.5 with improvements | 70B | Estimated 25-30GB (Q4) | 50-60GB | Expected to be a strong competitor. |
Llama 3 (Meta) | General purpose, conversation, writing, coding, reasoning | 8B, 13B, 70B+ | 4-6GB (8B), 7-9GB (13B), 25-30GB (70B) | 8-12GB (8B), 14-18GB (13B), 50-60GB (70B) | Current state-of-the-art open-source model. Excellent balance of performance and size. |
YiXin (01.AI) | Reasoning, brainstorming | 72B | ~26-30GB (Q4) | ~50-60GB | A powerful model focused on reasoning and understanding. Similar VRAM requirements to Qwen 72B. |
Phi-4 (Microsoft) | General purpose, writing, coding | 14B | ~7-9GB (Q4) | 14-18GB | Smaller model, good for resource-constrained environments, but may not match larger models in complexity. |
Ling-Lite | RAG (Retrieval-Augmented Generation), fast processing, text extraction | Variable | Varies with size | Varies with size | MoE (Mixture of Experts) model known for speed. Good for RAG applications where quick responses are important. |
Key Considerations:
This document details how to run Gemma models, covering framework selection, variant choice, and running generation/inference requests. It emphasizes considering available hardware resources and provides recommendations for beginners.
This article details a method for converting PDFs to Markdown using a local LLM (Gemma 3 via Ollama), focusing on privacy and efficiency. It involves rendering PDF pages as images and then using the LLM for content extraction, even from scanned PDFs.
This article compares the performance of smaller language models Gemma, Llama 3, and Mistral on reading comprehension tasks. The author highlights the trend of smaller, more accessible models and discusses Apple's recent foray into the field with its own proprietary model.
Gemma Scope is an open-source, multi-scale, high-throughput microscope system that combines brightfield, fluorescence, and confocal microscopy, designed for imaging large samples like brain tissue.
Explore the top small language models of 2024, including Llama 3, Phi 3, Mixtral 8x7B, Gemma, and OpenELM. Learn about their features, benefits, and significance in the AI landscape.
This article explains how to install Ollama, an open-source project for running large language models (LLMs) on a local machine, on Ubuntu Linux. It also covers the system requirements, installation process, and usage of various available LLMs.
Retrieval-Augmented Generation (RAG) is the concept of providing large language models (LLMs) with additional information from an external knowledge source. This allows them to generate more accurate and contextual answers while reducing hallucinations. In this article, we will provide a step-by-step guide to building a complete RAG application using the latest open-source LLM by Google Gemma 7B and Upstash serverless vector database.
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