Introduction
In today’s highly competitive e-commerce market, AI (Artificial Intelligence) technology has become a crucial tool for enhancing customer experience, optimizing operations, and increasing sales conversion rates. However, many businesses still rely on cloud-based AI services, which can be costly and pose concerns about data security and processing speed. Fortunately, there’s now a simpler, more cost-effective solution—deploying AI models directly on your local computer.
Why Does E-commerce Need Local AI?
- Protect Customer Data and Enhance Security Many e-commerce platforms collect and process vast amounts of user data, such as purchase history, search behaviors, and personal information. Running AI locally eliminates the need to upload this sensitive data to the cloud, reducing the risk of data breaches.
- Zero Latency for a Better Customer Experience Fast response times are critical in e-commerce operations. Whether it’s AI-driven customer support, personalized recommendations, or product searches, a locally deployed AI model can deliver millisecond-level responses. In contrast, cloud-based AI may suffer from delays due to network conditions.
- Lower Long-Term Costs Using cloud-based AI services often comes with ongoing subscription fees. In contrast, local AI requires only a one-time setup, with minimal running costs afterward. This makes it a cost-effective and efficient choice for small and medium-sized e-commerce businesses.
- Complete Control and Customization Cloud-based AI services are typically standardized and may not fully cater to individual business needs. By deploying AI locally, businesses can fine-tune AI functionalities, such as optimizing recommendation algorithms or training AI to better understand their specific customer base.
Deploying AI Has Never Been Easier!
Many people worry that setting up AI requires technical expertise. However, today’s tools have simplified the process to the point that even those with zero experience can easily handle it. Follow these simple steps to get AI running locally:
- Download Ollama, the core tool for local AI deployment.
- Check your computer configuration to ensure the GPU is suitable for running AI models.
- Install DeepSeek R1 with one click, selecting the model version that best fits your GPU capacity.
- Install the Web UI, allowing you to interact with AI directly through a web interface.
This entire process requires almost no technical knowledge—just follow the steps, and you’ll have AI running in no time. For e-commerce professionals, this is the best way to leverage AI to enhance business performance—both simple and highly effective.
This guide will walk you through each step of local AI deployment, helping you get started quickly and create your own AI assistant!
Step 1: Download and Install Ollama
Before deploying DeepSeek R1, you need to install Ollama. It is a required tool for managing AI models locally.
Download Ollama from its official website: https://ollama.com/
Follow the installation instructions to complete the setup.
Step 2: Check Your GPU Specifications
DeepSeek R1 requires a capable GPU for optimal performance. To check your GPU specifications:
- Press Win + R to open the “Run” window.
- Type dxdiag and click OK.
- Navigate to the Display tab.
- Identify your GPU name (e.g., NVIDIA, AMD, Intel HD Graphics).
Model Selection Based on GPU Memory:
| GPU Memory | Recommended Model |
|---|---|
| 8GB | 7b |
| 8GB+ | 8b |
| 12GB+ | 14b |
| 24GB+ | 32b |
| 48GB+ | 70b |
Step 3: Download and Run DeepSeek R1
Once Ollama is installed and you have determined your GPU capability, you can install DeepSeek R1 via the command line.
Open the Command Prompt (CMD) and use the appropriate command based on your GPU:
| Model | Command |
| 7b | ollama run huihui_ai/deepseek-r1-abliterated:7b |
| 8b | ollama run huihui_ai/deepseek-r1-abliterated:8b |
| 14b | ollama run huihui_ai/deepseek-r1-abliterated:14b |
| 32b | ollama run huihui_ai/deepseek-r1-abliterated:32b |
| 70b | ollama run huihui_ai/deepseek-r1-abliterated:70b |
Here is a table showcasing the open-source AI models currently supported by Ollama, along with their parameter sizes and download commands:
| Model Name | Parameter Size | Download Command |
|---|---|---|
| Llama 3.1 | 8B, 70B | ollama pull llama3.1:8b / ollama pull llama3.1:70b |
| Mistral | 7B | ollama pull mistral:7b |
| Phi-2 | 2.7B | ollama pull phi-2:2.7b |
| CodeGemma | 2B, 7B | ollama pull codegemma:2b / ollama pull codegemma:7b |
| Llama 2 | 13B, 70B | ollama pull llama2:13b / ollama pull llama2:70b |
| Orca Mini | 3B | ollama pull orca-mini:3b |
| LLaVA | 7B | ollama pull llava:7b |
| Gemma | 2B, 7B | ollama pull gemma:2b / ollama pull gemma:7b |
| Solar | 10.7B | ollama pull solar:10.7b |
📌 Full Ollama Model Library: Ollama Official Library
Step 4: Install Web UI for Easy Access
To interact with DeepSeek R1 using a graphical interface, install a Web UI extension:
Page Assist: A Web UI for Ollama
Once installed, you can easily use DeepSeek R1 from your Chrome browser.
Step 5: Uninstalling or Removing Installed Models
If you need to remove an installed model, use the following command:
ollama rm <model-name>:<version>For example, to remove the 70b model, execute:
ollama rm deepseek-r1:70bFor example, to remove theLLama 3 8B model, execute:
ollama rm llama3:8bCheck Installed Models
If you’re unsure which models are installed, use:
bashCopyEditollama list
This will display a list of all installed models, making it easier to choose which one to remove.
📌 Note: Once removed, the model will need to be re-downloaded if you want to use it again. Make sure you no longer need it before deleting.
Conclusion
By following these steps, you can deploy DeepSeek R1 on your local machine, taking full control of your AI capabilities. Whether you’re using it for development, research, or personal AI assistance, this setup ensures a seamless experience.