High-Performance LLMs, Now on Your PC! Top 10 Recommended Free Local Open-Source LLMs

G: Hello, AI era inhabitants! 🤖✨ In these days when we can’t help but be amazed by the latest technology, did you know that you can directly run high-performance Large Language Models (LLMs) on your own computer? 🤯 And it’s ‘free’!

While cloud-based LLM services are convenient, they come with drawbacks such as cost burden, concerns about personal data leakage, and the necessity of an internet connection. However, locally runnable open-source LLMs alleviate these worries, allowing you to have your own AI assistant, creative tool, or learning partner.

Today, we recommend 10 powerful and attractive open-source LLMs that you can fully utilize on your PC. Now, without worrying about expensive subscriptions or data breaches, create your own AI playground! 🚀

💡 Why Local LLMs? Essential Advantages You Must Know!

Running an LLM locally goes beyond just looking cool; it offers practical benefits.

• 🔒 Excellent Personal Data Protection and Security: When handling important documents or sensitive data, there’s no need to transmit data to cloud servers, eliminating the risk of data leakage. All operations happen within your PC!
• 💸 Cost Savings: Once downloaded, you can use it forever for free, without API usage fees or subscription costs.
• 🌐 Offline Accessibility: You can use the LLM anytime, even without an internet connection. No problem on an airplane or where there’s no Wi-Fi!
• 🛠️ Unlimited Customization: You can fine-tune the model for your specific purposes or inject specific knowledge data to create your own specialized LLM.
• 🧑‍💻 Freedom to Learn and Experiment: For developers and AI learners, it’s the best environment to freely experiment with and learn about the model’s internal workings, prompt engineering, and more.

✨ Preparations Needed for Local Execution (Briefly!)

Before we dive into the models, let’s briefly look at what you need to run LLMs locally.

• Computer Specifications: Generally, higher RAM (16GB or more recommended, 32GB or more for smoother experience) and GPU (graphics card) VRAM (8GB or more recommended, 12GB or more is good for high-performance models!) allow you to run larger models faster. However, even with just a CPU, smaller models are definitely possible!
• Software: You’ll need programs to help run the models. Popular options include Ollama, LM Studio, Jan.ai, and text-generation-webui, which allow you to easily load and use optimized model formats like GGUF.

Alright, now let’s go meet the 10 open-source LLMs ready to be brought to life on your PC! 🌟

🚀 Top 10 High-Performance Open-Source LLMs for Your PC!

Most of these models are provided in optimized formats like ‘GGUF’, allowing them to run efficiently on a CPU or with less GPU VRAM.

1. Meta Llama 3 (8B, 70B, etc.) 🐐

• Description: The latest open-source LLM series released by Meta, Facebook’s parent company, succeeding Llama 2. Especially the Llama 3 8B model is very suitable for personal PC execution while showing surprising performance. The 70B model is more powerful but requires more VRAM.
• Features:
  • Overwhelming Performance: Significantly improved overall performance in inference, coding, and multilingual capabilities compared to Llama 2.
  • Various Sizes: Offers various sizes including 8B, 70B, and even 400B (in training), allowing selection based on your specifications.
  • Strong Community: Has the most active community and numerous fine-tuned models.
• Recommended Use Cases:
  • Idea Brainstorming: “Suggest 5 new business ideas.”
  • Content Draft Creation: Blog posts, emails, marketing copy, etc.
  • Coding Assistance: Writing and debugging code in specific programming languages.
  • Complex Question Answering: Wide range of answers from general knowledge to specialized information.
• Tip: The 8B model can run comfortably with 16GB RAM and 8GB VRAM, and can be easily downloaded and run via Ollama or LM Studio. ollama run llama3

2. Mistral 7B & Mixtral 8x7B 🌬️

• Description: Models developed by Mistral AI from France, boasting excellent performance despite their small size. Mixtral 8x7B, in particular, uses a Mixture of Experts (MoE) structure to achieve both efficiency and performance simultaneously.
• Features:
  • Mistral 7B: A miraculous model that is smaller than Llama 2 13B but performs much better.
  • Mixtral 8x7B: Uses an MoE structure that activates 2 out of 8 expert models for inference, achieving 45B parameter-class performance with only about 13B parameter-level resources. (Actual size is 45B-class)
  • Fast Inference Speed: Very fast inference is possible due to its efficient structure compared to models of similar performance.
• Recommended Use Cases:
  • Simple Script Writing: “Write a Python script to list files.”
  • Multilingual Translation: English-Korean translation, learning specific language expressions.
  • Chatbot Implementation: Lightweight conversational interfaces.
  • Summarization: Summarizing long documents or articles.
• Tip: Mixtral 8x7B recommends approximately 24GB of RAM and 10-12GB or more of VRAM, but can also run on CPU through GGUF quantization. ollama run mistral or ollama run mixtral

3. Google Gemma (2B, 7B) 💎

• Description: A lightweight open model developed by Google based on the technology of its cutting-edge AI model, Gemini.
• Features:
  • Google’s Technology: Incorporates technical know-how gained from Gemini research, resulting in excellent performance.
  • Responsible AI: Safety and ethical considerations are reflected in the training process.
  • Lightweight: Both 2B and 7B models are suitable for running on personal PCs.
• Recommended Use Cases:
  • Educational Chatbot: Answering students’ questions or generating learning materials.
  • Simple Information Retrieval and Answering: Summarized information on specific topics.
  • Sentence Completion and Improvement: Writing assistance.
• Tip: The 7B model shows similar performance to Llama 2 13B but runs lighter. ollama run gemma

4. Microsoft Phi-3 Mini (3.8B) 🧠

• Description: An ultra-compact language model developed by Microsoft. It boasts surprising performance despite its very small size and is designed to run on mobile devices or low-spec PCs.
• Features:
  • Amazing Efficiency: Can perform complex inference tasks with a small number of parameters.
  • High-Quality Training Data: Trained with carefully selected high-quality data, making efficient learning possible, earning it the nickname “small giant.”
  • Accessibility: Optimal for users who want to experience LLMs with minimal resources.
• Recommended Use Cases:
  • On-device AI: Implementing AI functions in limited environments such as smartphones, embedded devices.
  • Learning on Very Low-Spec PCs: LLM execution testing and conceptual learning.
  • Simple Text Generation and Summarization: Short writing assistance.
• Tip: The 3.8B model can run sufficiently with just 8GB RAM, and can be used by searching for phi3 or phi3:mini in Ollama or LM Studio.

5. Qwen 1.5 (7B, 14B) 🇨🇳

• Description: A powerful open-source LLM series developed by Alibaba’s Tongyi Qianwen research team in China. It is particularly strong in multilingual support.
• Features:
  • Multilingual Performance: Shows excellent performance not only in English but also in various other languages, including Chinese.
  • Various Model Sizes: Offers a wide range of sizes from 0.5B to 72B for flexible selection.
  • Coding Ability: Excellent in understanding programming languages and generating code.
• Recommended Use Cases:
  • Multilingual Content Generation: Writing and translating text in multiple languages.
  • Asian Language Processing: Tasks based on Asian languages such as Chinese, Korean.
  • Professional Q&A: Interpreting and summarizing technical documents in specific fields.
• Tip: The 7B model recommends 16GB RAM and 8GB VRAM or more, and can be started with ollama run qwen.

6. 01.AI Yi (6B, 9B) 🐅

• Description: The Yi series, developed by 01.AI, is a notable open-source LLM from China, attracting attention for its outstanding performance. It achieves very high benchmark scores compared to other models with the same number of parameters.
• Features:
  • Excellent Benchmark Performance: Ranks among the top performers in its class across various evaluation metrics.
  • Efficient Architecture: Has an efficient architecture that can achieve high performance with fewer parameters.
  • Strong for General Purposes: Shows excellent capabilities in general conversation, writing, and information summarization.
• Recommended Use Cases:
  • General Productivity Tasks: Email writing, report drafts, meeting minute summarization.
  • Creative Writing: Novel ideas, poems, scenario drafts.
  • Questions Requiring Complex Logical Reasoning: Puzzle solving, logical thinking challenges.
• Tip: Both 6B and 9B models are suitable for running on personal PCs. You can find and use the GGUF version on Hugging Face or model distribution platforms.

7. TinyLlama 1.1B 🐥

• Description: An ultra-lightweight model with an extremely small number of parameters (1.1 billion), trained based on the Llama 2 architecture. It’s suitable when you want to experience LLMs with truly minimal resources.
• Features:
  • Extreme Lightweight: Can run on almost any PC. Sometimes even runs with about 4GB RAM.
  • Fast Inference Speed: Because it’s small, inference speed is very fast.
  • Llama 2 Based: Inherits the Llama 2 structure, offering good scalability and compatibility.
• Recommended Use Cases:
  • LLM Testing on Low-Spec Devices: Old laptops or mini PCs, etc.
  • Very Simple Text Generation: Short sentence completion, word prediction.
  • Education and Concept Understanding: Experiencing how LLMs work.
• Tip: Performance is limited, but it’s the least demanding model for setting up and testing an LLM environment. ollama run tinyllama

8. OpenHermes 2.5/DPO (Mistral/Llama-based fine-tuning) 🦉

• Description: A fine-tuned model primarily based on Mistral 7B or Llama 2 7B/13B, specialized in understanding instructions and generating complex conversations. “Hermes” is known for its excellent inference and instruction-following abilities.
• Features:
  • Excellent Instruction Following: Accurately understands user prompts and generates responses that match the intent.
  • Conversational Ability: Strong in maintaining natural and consistent conversations.
  • Various Variants: Many variants with latest technologies like DPO (Direct Preference Optimization) are available, constantly improving performance.
• Recommended Use Cases:
  • Conversational AI Assistant: Interacting naturally with users and providing information.
  • Complex Prompt Interpretation: Accurate responses to prompts containing multiple instructions.
  • Role-playing and Simulation: Conversation assuming a specific person or situation.
• Tip: Can be run if your system meets the specifications of the base models Mistral 7B or Llama 2 7B. Search for openhermes on Hugging Face to find GGUF versions.

9. Zephyr-7B-beta (Mistral-based fine-tuning) 💨

• Description: Developed by Hugging Face, Zephyr is a model based on Mistral 7B that shows high performance particularly optimized for chatbot applications. It was one of the very popular models early on.
• Features:
  • Specialized in Chatting: Excellent in conversation generation and understanding user intent.
  • Powerful Despite Being Lightweight: Very good chatbot performance for its small size.
  • User-Friendly: Maintains naturalness in everyday conversations.
• Recommended Use Cases:
  • Personal Conversational Assistant: Managing schedules or providing information.
  • Simple Customer Support Chatbot Prototype: Answering frequently asked questions.
  • Idea Exchange Partner: Creative conversation simulation.
• Tip: Can be run with similar specifications as Mistral 7B. Can be easily used with the ollama run zephyr command.

10. Nous Hermes 2 – Mixtral 8x7B DPO 🧠✨

• Description: A model fine-tuned using the Mixtral 8x7B from Mistral AI, with dataset and DPO (Direct Preference Optimization) techniques. It boasts top-tier performance among models runnable locally.
• Features:
  • Mixtral’s Strengths + Fine-tuning: Combines Mixtral’s efficiency with Nous Research’s fine-tuning expertise, making it extremely powerful.
  • High Reasoning Ability: Shows excellent ability in solving complex problems and logical reasoning.
  • Instruction Understanding and Generation Quality: Accurately understands user instructions and generates high-quality responses.
• Recommended Use Cases:
  • Advanced Coding Assistance: Designing complex algorithms, Q&A on specific framework usage.
  • In-depth Research and Analysis Support: Summarizing academic materials, expanding ideas.
  • Creative and Complex Writing: Storytelling, scenario development.
  • Personal Tutor: In-depth questions and answers on specific topics.
• Tip: Recommends slightly higher VRAM than Mixtral 8x7B (about 12GB or more), but can run comfortably on CPU through GGUF quantization. If you want to experience the ultimate LLM, definitely give it a try!

🛠️ How to Get Started with Local LLMs? (Quick Guide for Beginners)

Want to run these models on your computer right away? Here’s the easiest and fastest way to get started!

1. Install Ollama:

   • Ollama is a tool that allows you to easily run LLMs via CLI (Command Line Interface) or API without complex configurations.
   • Download and install the version compatible with your OS (Windows, macOS, Linux) from the official website (ollama.com).
   • After installation, open your terminal (command prompt) and try running your desired model.
     • Example: ollama run llama3
     • (The first run will automatically download the model. It might take some time!)
   • After downloading, a >>> send a message prompt will appear, and you can start chatting with the AI immediately.

2. Install LM Studio:

   • LM Studio is a GUI (Graphical User Interface) based program that allows you to easily search, download, and run LLMs just like an app.
   • Download and install it from the official website (lmstudio.ai).
   • After launching, search for your desired model in the left search tab, download it, and then load it in the right chat tab to start a conversation.
   • You can choose various quantization versions of models to pick the optimal one for your system specifications.

3. Find GGUF Models on Hugging Face:

   • Most open-source LLMs are available on the Hugging Face Hub (huggingface.co/models).
   • Here, you can find and download the GGUF version of your desired model using the GGUF tag or search term, and then load it into other tools like LM Studio or text-generation-webui.

🎁 Concluding Remarks: Open Your Own AI Era!

Today, we introduced 10 attractive open-source models that allow you to run high-performance LLMs on your computer for free. ✨ AI is no longer a technology far removed, owned by giant corporations. It has become a tool in your hands, capable of unfolding infinite possibilities according to your needs.

Of course, running local LLMs requires some hardware specifications, but you can start with smaller models like TinyLlama or Phi-3 Mini and gradually expand to more powerful models.

Install Ollama or LM Studio right now and begin your first conversation with a local LLM! 🗣️ Privacy protection, cost savings, and the freedom of unlimited customization! We strongly recommend you enjoy all these benefits and brightly open your own AI era! 🥳

If you have any questions, please leave them in the comments! We hope it becomes a growing AI journey together. 💖