High-Performance LLMs on Your Computer for Free! Top 10 Open-Source LLMs You Can Run Locally

D: Recently, more people want to run large language models (LLMs), the core of conversational AI, directly on their computers. 🌟 The advantage is that you can interact with AI offline and handle various tasks without relying on cloud services! In this post, we introduce 10 open-source LLMs you can use for free on your local PC.

🔍 Benefits of Local LLMs

1. Privacy Protection 🔒: No data is sent to external servers
2. Offline Use 📴: AI is available without an internet connection
3. Customization 🛠️: Modify models to suit your needs
4. Cost Savings 💰: No cloud API usage fees

💻 System Requirements

To run most LLMs smoothly:

• RAM: Minimum 16GB (32GB+ recommended)
• GPU: NVIDIA GPU (RTX 3060+ recommended)
• Storage: 5–30GB free space per model

🏆 Top 10 Open-Source LLMs for Local Execution

1. LLaMA 2 (Meta)

• Features: Next-gen open-source LLM released by Meta (Facebook)
• Versions: 7B, 13B, 70B parameter models
• Pros: Commercial use permitted, foundation for various derivative models
• Recommended Tool: Optimized CPU/GPU execution via llama.cpp

2. Mistral 7B

• Features: Delivers 72B-level performance in a 7B-sized model
• Strengths: Excellent reasoning, strong in English/French
• Execution: Easily loaded via Hugging Face Transformers

3. Falcon (180B/40B/7B)

• Features: Developed by UAE’s Technology Innovation Institute (TII)
• Pros: 180B version is the largest open-source LLM
• Note: Running 180B requires multiple high-end GPUs

4. Vicuna (7B/13B)

• Features: LLaMA-based model specialized for conversations
• Strengths: Natural dialogue comparable to ChatGPT
• Execution: 13B model runs on 24GB RAM with 4-bit quantization

5. Alpaca (Stanford)

• Features: LLaMA 7B fine-tuned for educational purposes
• Pros: Excels at understanding simple instructions
• Use Case: Ideal for research and education

6. GPT4All

• Features: Integrated open-source ecosystem of multiple models
• Pros: User-friendly GUI included
• Models: Includes LLaMA-based models, runs on CPU

7. RWKV (Raven)

• Features: Innovative RNN-based architecture (non-Transformer)
• Pros: Low memory usage, excels at long-context processing
• Versions: 1B5, 3B, 7B, 14B, etc.

8. OpenAssistant (OASST)

• Features: Community-developed conversational AI
• Strengths: Supports 35 languages, human-like responses
• Models: Includes Pythia-based 12B version

9. RedPajama

• Features: Trained on LLaMA-compatible open datasets
• Versions: 3B, 7B, 65B models available
• Pros: Fully open-source (data + model + code)

10. StableLM (Stability AI)

• Features: Developed by Stability AI (creators of Stable Diffusion)
• Traits: 3B–65B models, uses open datasets
• Strengths: Great for creative text generation

🛠️ Tools for Running Local LLMs

1. Text Generation WebUI:

   • Oobabooga's Text Generation WebUI (most popular)
   • KoboldAI (specialized for RPG-style dialogue)

2. Optimization Libraries:

   • llama.cpp (CPU-optimized)
   • AutoGPTQ (GPU quantization support)

3. Easy-Install Packages:

   • LM Studio (GUI for Windows/macOS)
   • GPT4All (all-in-one solution for beginners)

💡 Tips for Beginners

1. Start small: Test with 7B parameter models first
2. Use quantization: 4-bit quantization reduces VRAM requirements
3. Try CLI versions: Lower resource usage than GUI
4. For older GPUs: Prevent VRAM overflow with --pre_layer option

🚀 Future Outlook

In 2024, we expect even lighter yet more powerful models. Ultra-lightweight models ( Note: Always check licenses before downloading models. Most are for research use only. Commercial use may require additional permissions.