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## Local Model Deployment Many people think AI can only be used in the cloud. When using ChatGPT, data is sent to OpenAI's servers. When using Claude, requests are sent to Anthropic's data center. In fact, AI models can run completely on your own computer. Imagine: using AI without internet, sensitive data never leaves your computer, no need to pay per call, and complete customization freedom. This is the value of local model deployment. This module will take you from scratch to running your first local AI model on your own computer. * * * ## Why Local AI is Needed Cloud AI is convenient, but local AI has irreplaceable advantages. ### Data Privacy Protection This is the primary reason many people choose local deployment. If you're processing contracts, medical records, financial data, or internal documents, sending this data to third-party servers carries risks. When running locally, all data is processed on your own computer and never leaves your device. ### Offline Usage On airplanes, trains, or places with unstable internet, cloud AI won't work. After downloading a local model once, it's permanently available without internet connection. ### Cost Control Cloud AI charges per call or per token, which can become a significant expense with heavy usage. Local models are downloaded once and can be used unlimited times without additional fees. ### Customization Possibilities Cloud models can't be modified; you can only use the features provided. Local models can be fine-tuned, quantized, and prompt templates can be modified to fully customize according to your needs. These advantages comparison: | Feature | Cloud AI | Local AI | | --- | --- | --- | | Data Privacy | Data needs upload | Data fully localized | | Network Dependency | Must be online | No network required | | Usage Cost | Pay per use | One download, unlimited use | | Customization | Limited | Fully controllable | | Convenience | Ready to use | Requires configuration | | Response Speed | Depends on network | Depends on hardware | > This is not to say cloud AI is bad, but rather that **each has its applicable scenarios**. Use cloud for casual chat, use local for sensitive data; use cloud for simple tasks, use local for special customizations. * * * ## Hardware Requirements Assessment Whether a local model can run and how fast it runs depends mainly on your hardware configuration. ### CPU Inference vs GPU Inference There are two ways to run models: CPU inference and GPU inference. CPU inference runs the model using the computer's main processor. The advantage is good compatibility—almost any computer can run it. The disadvantage is slow speed; large models may take a long time to process. GPU inference uses the graphics card. The core advantage is strong parallel computing capability, running models dozens of times faster. For NVIDIA graphics cards, you need to install the CUDA toolkit; for AMD cards, you can use ROCm; for Intel cards, you can use OpenVINO. ### VRAM Requirements and Model Size Relationship Video RAM (VRAM) determines how large a model you can run. More model parameters require more VRAM. However, through quantization technology, larger models can be run with less VRAM. | VRAM Size | Runnable Models (FP16) | Runnable Models (Quantized) | Recommended Scenario | | --- | --- | --- | --- | | 8 GB | 7B model is marginal | 7B (Q4), 13B (Q4) marginal | Simple dialogue, lightweight tasks | | 16 GB | 7B, 13B models | 7B, 13B (Q4/Q8), 33B (Q4) | Daily use, best value | | 24 GB | 7B, 13B, 33B models | 7B, 13B, 33B (Q4/Q8), 70B (Q4) | Professional use, smooth experience | | 48 GB+ | 33B, 70B models | All mainstream models | Research, production environment | ### Advantages of Mac M Series Apple Silicon (M1, M2, M3 series chips) has unique advantages for local AI. Apple's Metal framework and unified memory architecture make Macs very efficient at running local models. Unified memory means CPU and GPU share memory, and when VRAM is insufficient, it can automatically borrow from memory. A MacBook Pro with 16GB unified memory can smoothly run quantized 7B and 13B models. ### Recommended Configuration Options Based on different budgets and needs, here are several configuration options: | Option | Configuration | Suitable For | Expected Experience | | --- | --- | --- | --- | | Entry Level | Any computer (8GB+ RAM) | Beginners wanting to try local AI | Can run small models, slower speed | | Best Value | 16GB RAM + 6GB+ VRAM | Personal daily use | 7B/13B models run smoothly | | Professional | 32GB RAM + 12GB+ VRAM | Developers, researchers | 33B model runs smoothly | | Mac Option | M1/M2/M3 + 16GB unified memory | Mac users | 7B/13B models run smoothly | > Don't be intimidated by "large models." Today's quantization technology already allows 7B models to run smoothly on regular computers, and 7B model capabilities are sufficient for most daily tasks. * * * ## Ollama: The Simplest Local AI Tool * Ollama Official Website: [https://ollama.com/](https://ollama.com/) * Ollama Supported Models: [https://ollama.com/search](https://ollama.com/search) * Ollama Tutorial: [https://example.com/ollama/ollama-tutorial.html](https://example.com/ollama/ollama-tutorial.html) Ollama is currently the most popular local model tool. In one sentence: install models like installing apps, use AI like using command line. ### Installation and Configuration Ollama supports macOS, Linux, and Windows. The installation process is very simple. Installation package download address: [https://ollama.com/download](https://ollama.com/download) One command to install: curl -fsSL https://ollama.com/install.sh | sh macOS users can directly download the installation package, or use Homebrew to install: # macOS use Homebrew to install brew install ollama # Or download the installation package: https://ollama.com/download Windows users can directly download the installation package from the official website and run it. After installation, start the Ollama service: ollama serve Once you see the service started successfully, you can start using it. ### Downloading Models Ollama's model library is very rich, including mainstream models like Llama, Qwen, Gemma, Mistral, etc. Downloading a model only requires one command: ## Example # Download Llama 3 (8B parameters) ollama pull llama3 # Download Qwen (Tongyi Qianwen, Chinese-friendly) ollama pull qwen # Download Gemma (from Google) ollama pull gemma # Download Mistral ollama pull mistral You can also use the `ollama run + model name` command; it will automatically download if the specified model doesn't exist: ollama run qwen Run the qwen model, will download if not available. Each model has different versions, such as 8B, 70B, or different quantized versions. You can use `ollama list` to view downloaded models: ollama list Output similar to: NAME ID SIZE MODIFIED qwen3.5:latest 6488c96fa5fa 6.6 GB 3 months ago ### Command Line Usage After downloading the model, you can run it directly to start a conversation: # Run Llama 3, will download if not available ollama run llama3 # Run Qwen, will download if not available ollama run qwen3.5 qwen3.5 is more than sufficient for ordinary tasks: !(https://example.com/wp-content/uploads/2026/06/1782050331856.png) Then you can directly chat with the model: >>> Hello, please introduce yourself Hello! I am Llama 3, an AI assistant developed by Meta. I can help you answer questions, write, program, analyze data, etc. What can I help you with? >>> Write a Python Hello World program Sure, here is a simple Python Hello World program: print("Hello, World!") >>> /bye # Enter /bye to exit Common commands: | Command | Function | Example | | --- | --- | --- | | ollama pull | Download model | ollama pull llama3 | | ollama run | Run model | ollama run llama3 | | ollama list | List installed models | ollama list | | ollama rm | Delete model