High-Performance Worries Over! Introducing 10 Open-Source LLMs You Can Run Even on Low-Spec PCs

G: Recently, artificial intelligence (AI) has become an essential part of our lives. Large Language Models (LLMs) like ChatGPT demonstrate amazing abilities to answer questions, write text, and generate code. 🤯 However, many people think that running such LLMs directly requires an incredibly high-performance PC. Expensive GPUs with tens of gigabytes of VRAM, as well as ample RAM and CPU power… 💸 This often feels like a significant barrier for average users.

But don’t worry! 🙌 Open-source LLMs are now emerging that can be fully utilized even on low-spec PCs. These models are small in size but show astonishing performance, allowing you to experience the magic of AI on your laptop or desktop. Today, we’ll introduce 10 low-spec PC-friendly open-source LLMs that will alleviate your high-performance worries! 💻✨

💡 Why are low-spec PC LLMs important?

• Improved Accessibility: Anyone can access AI technology without high-performance hardware. 🚀
• Privacy Protection: Unlike cloud-based services, running models on a personal PC eliminates the concern of sensitive information being sent to external servers. 🔒
• Cost Efficiency: Reduces the burden of expensive cloud API usage fees or high-cost hardware purchases. 💰
• Offline Use Capability: AI can be utilized anytime, anywhere, even without an internet connection. ✈️

🤔 What are the criteria for “low-spec friendly” LLMs?

“Low-spec friendly” here primarily refers to models with the following characteristics:

• Fewer Parameters: The model is smaller, requiring less memory (RAM, VRAM). Models with typically 7B (7 billion) parameters or less fall into this category.
• Efficient Architecture: Even with the same number of parameters, they are designed more efficiently to deliver good performance with fewer resources.
• Ease of Quantization: The precision of the model can be reduced (e.g., from 16-bit to 4-bit) to significantly decrease file size and required memory. Models converted to formats like GGUF, AWQ, EXL2 fall into this category.

Generally, 4-bit quantized 7B models can be sufficiently run on PCs with 8GB~16GB RAM, and even without VRAM, they can be powered using CPU memory.

🛠️ How to Run LLMs on Low-Spec PCs (Brief Summary)

• Ollama: The easiest and most convenient method. You can download and run models with a single command like ollama run mistral. Multi-platform support.
• LM Studio / LoLLMs: GUI-based applications that allow you to download various models and use them directly in a chat UI without complex settings. (Windows, macOS)
• llama.cpp-based tools: A library that efficiently runs GGUF format models on the CPU. It’s good to use with GUI front-ends like Text Generation WebUI (oobabooga).
• Hugging Face transformers library: For Python developers, you can use Hugging Face’s transformers library to load models with quantization options and write your own code to run them.

🌟 High-Performance Worries Over! Introducing 10 Open-Source LLMs You Can Run Even on Low-Spec PCs

Now, let’s dive into the main topic and meet 10 gem-like LLMs that will transform your low-spec PC into an AI workstation! 💎

1. Mistral 7B 🌬️

• Features: Developed by French AI startup Mistral AI, this model has 7 billion parameters (7B) yet performs comparably to, and sometimes even surpasses, 13B models. Its overwhelming efficiency has earned it the nickname “the little giant.”
• Low-Spec Friendliness: When 4-bit quantized, it can run with less than 8GB of RAM/VRAM, offering the most stable and excellent performance on low-spec PCs. 🚀
• Main Uses: Excellent for a wide range of tasks including general question answering, text summarization, and code generation. There are numerous Mistral-based fine-tuned models available, offering a wide selection.
• Example: “Tell me the code to create a simple web server in JavaScript.” or “Explain the Fibonacci sequence.”

2. Phi-3 Mini 🧠

• Features: Microsoft’s latest in the “small but mighty” model series. It has 3.8 billion parameters (3.8B), and despite its small size, its reasoning ability and language comprehension are outstanding. It shows particularly strong performance in mathematics and coding.
• Low-Spec Friendliness: When 4-bit quantized, it can run smoothly with 4GB~6GB RAM/VRAM, making it especially advantageous for low-spec devices like portable laptops. 🎒
• Main Uses: Complex problem-solving, logical reasoning, coding assistance, data analysis, etc.
• Example: “Prove the Pythagorean theorem.” or “Tell me an efficient way to sort a list in Python.”

3. Gemma 2B ✨

• Features: An open model developed by Google, with 2 billion parameters (2B). It incorporates Google’s latest technology, offering decent performance, excellent security, and adhering to responsible AI principles despite its small size.
• Low-Spec Friendliness: When 4-bit quantized, it can run with less than 4GB of RAM/VRAM, allowing you to experience Google’s technology even on a low-spec PC. 💡
• Main Uses: Suitable for basic LLM tasks such as light conversation, information retrieval, and text generation.
• Example: “What’s the weather like today?” (Of course, not real-time data) or “Write a simple thank-you message.”

4. TinyLlama 1.1B 🤏

• Features: As its name suggests, this is a ‘tiny’ model trained based on the Llama 2 architecture. It has 1.1 billion parameters (1.1B) and shows surprising potential despite being trained on a limited dataset.
• Low-Spec Friendliness: One of the smallest and lightest models introduced today, it can run sufficiently with just 2GB~4GB RAM/VRAM, allowing it to operate on extremely low-spec PCs. 🐢
• Main Uses: Good for simple text generation, explaining concepts, and using in testing and development environments.
• Example: “What is a computer?” or “Create a funny short story.”

5. Qwen 1.8B ✍️

• Features: Developed by Alibaba Cloud, this model has 1.8 billion parameters (1.8B). Its strength lies in its support for various languages, including Korean, and it boasts an efficient architecture.
• Low-Spec Friendliness: When 4-bit quantized, it shows good performance with less than 4GB of RAM/VRAM, and its excellent multilingual processing capability makes it useful for experiencing global AI on a low-spec PC. 🌍
• Main Uses: Multilingual translation, text generation and comprehension in various languages, general conversation, etc.
• Example: “Translate this sentence into French: ‘Hello, I’m from Korea.'”

6. StableLM-2 1.6B 🎨

• Features: A text model developed by Stability AI, famous for its image generation AI. It has 1.6 billion parameters (1.6B) and has gained attention for its concise yet efficient design.
• Low-Spec Friendliness: When 4-bit quantized, it can run with less than 4GB of RAM/VRAM, making it suitable for low-spec environments where you want to use text and image AI simultaneously with models like Stable Diffusion. 🖼️
• Main Uses: Idea brainstorming, short text generation, chatbot prototype development, etc.
• Example: “Suggest 3 creative story ideas.”

7. Llama 2 7B 🦙

• Features: The smallest model (7 billion parameters) in the Llama 2 series developed by Meta. It boasts powerful base performance, having been trained on vast amounts of data, and is one of the most widely used and fine-tuned models.
• Low-Spec Friendliness: Like Mistral 7B, it can be run sufficiently with less than 8GB of RAM/VRAM when 4-bit quantized, with the added advantage of abundant resources and community support. 🤝
• Main Uses: Highly versatile for general conversation, text summarization, translation, education, and various other fields.
• Example: “Draft an email.” or “Summarize what I learned today.”

8. Neural-Chat-7B-v3 💬

• Features: A conversational model fine-tuned by Intel based on Mistral 7B. It specifically focuses on providing accurate and helpful answers to user queries.
• Low-Spec Friendliness: Being Mistral 7B-based, it exhibits excellent conversational performance with less than 8GB of RAM/VRAM when 4-bit quantized. 🗣️
• Main Uses: Suitable for chatbot applications, customer service, information retrieval, and building Q&A systems.
• Example: “Explain the latest AI trends.” or “Recommend a travel destination suitable for me.”

9. OpenHermes 2.5 Mistral 7B 📜

• Features: A model fine-tuned on the “idea” dataset (OpenHermes) based on Mistral 7B. It shows outstanding performance in various areas such as creative writing, idea generation, and logical reasoning.
• Low-Spec Friendliness: Also Mistral 7B-based, it can be fully utilized with less than 8GB of RAM/VRAM when 4-bit quantized. ✍️
• Main Uses: Specialized in creative tasks such as novel writing, scenario planning, marketing copy creation, and acting as a brainstorming partner.
• Example: “Suggest 3 plot ideas for a sci-fi novel.” or “Create a slogan for a company’s new product launch.”

10. CodeLlama 7B 🧑‍💻

• Features: A model fine-tuned based on Meta’s Llama 2, specializing in code generation and understanding. It helps in understanding various programming languages, writing code, and debugging.
• Low-Spec Friendliness: When 4-bit quantized, it performs sufficiently to assist with programming tasks, even with less than 8GB of RAM/VRAM. 👨‍💻
• Main Uses: Code auto-completion, function generation, code debugging, converting code to a specific language, explaining programming concepts, etc.
• Example: “Show me an example code for web scraping in Python.” or “How do I handle asynchronous processing in JavaScript?”

🚀 Tips for Maximizing LLM Performance on Low-Spec PCs!

1. Quantization is Essential: In most cases, using a 4-bit quantized model is the most efficient. Look for model filenames with .gguf, _q4_0.gguf, _awq, etc.
2. Use Appropriate Tools: Utilize tools optimized for CPUs or low-spec GPUs, such as Ollama, LM Studio, or llama.cpp.
3. Close Background Apps: To secure as much RAM and CPU resources as possible when running an LLM, it’s best to close unnecessary programs.
4. Adjust Batch Size: You can reduce memory usage by decreasing the number of tokens processed at once. (If configurable)
5. Limit Prompt Length: Overly long prompts require more memory and time. Convey necessary information concisely.

🎉 Conclusion: AI is No Longer the Exclusive Domain of High-Performance Machines!

As you’ve seen, there’s no reason to refrain from stepping into the world of LLMs just because you don’t have a high-spec PC. The models introduced today perform astonishingly well even in low-spec environments, opening the door wide for you to integrate AI into your daily life and work. 🚪✨

Now, you can build and run your own AI assistant, creative helper, or learning partner directly on your old laptop or regular desktop. Challenge yourself now and start your own AI experience! 🚀💡