How to Use DeepSeek on Your Computer: A Step-by-Step Guide to Running LLMs Locally
Marcus Magarian
Strategic Advisor | Helping European Companies Access US Markets | Host of The Exit Strategy Podcast
In recent years, artificial intelligence has made significant strides, particularly in the development of large language models (LLMs). These models, such as OpenAI's ChatGPT and DeepSeek's R1, have transformed how we interact with AI, enabling everything from creative writing to technical problem-solving.
While many of these models are hosted on cloud-based platforms, there is a growing interest in running them locally on personal computers. Running an LLM locally can provide greater privacy, offline accessibility, and cost savings over time. This guide will walk you through the process of downloading and running DeepSeek's R1 on your computer using an application like ChatbotAI. We'll also explore whether running ChatGPT locally is feasible and what alternatives exist.
Why Run an LLM Locally?
Running a large language model (LLM) on your local machine offers several key advantages:
? Privacy & Security – Your data stays on your device rather than being sent to a remote server, reducing exposure to potential data breaches or unauthorized access.
? Offline Access – You can use the model even without an internet connection, which is useful for developers, researchers, or anyone working in remote areas.
? Cost Efficiency – Cloud-based AI services often come with subscription fees and usage-based costs. Running an LLM locally eliminates these ongoing expenses.
? Customization & Control – Locally hosted models allow you to modify and fine-tune responses, optimize performance for specific tasks, and avoid cloud-imposed restrictions.
? Reduced Censorship Issues – When you run an LLM on your computer, you bypass the restrictions and filters that may be imposed by cloud-based services. This allows for unfiltered model interactions.
Prerequisites: System Requirements
Before setting up DeepSeek R1, ensure that your computer meets the minimum requirements for running an LLM:
?? Hardware Requirements
??? Operating System
?? Software Requirements
Step 1: Downloading DeepSeek R1
The first step is to obtain the DeepSeek R1 model.
1?? Visit the DeepSeek Website – Go to DeepSeek's official website and navigate to the downloads section.
2?? Download the R1 Model – Select the model version that best suits your system. The model will typically be available in .zip or .tar.gz format.
3?? Extract the Files – Unzip the downloaded model files into a dedicated folder on your computer.
There are Multiple Models to Choose from:
Step 2: Setting Up the Environment
Before running DeepSeek R1, you need to set up the necessary dependencies.
1. Install Python
If you don’t have Python installed:
sudo apt update && sudo apt install python3 python3-pip
? Ensure that Python is added to your system’s PATH during installation.
2. Install Required Python Libraries
Open a terminal (Command Prompt, PowerShell, or Terminal) and run:
pip install torch transformers numpy
If using an NVIDIA GPU, install CUDA and cuDNN for faster processing.
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Step 3: Running DeepSeek R1 with ChatbotAI
Once the environment is set up, we need a front-end application to interact with the LLM.
1. Download ChatbotAI
Visit the ChatbotAI GitHub repository (or the official website) and download the latest release.
2. Configure ChatbotAI
3. Load & Run the Model
Launch ChatbotAI and load DeepSeek R1. The model may take a few minutes to initialize.
? You can now chat with DeepSeek R1 by entering text in the interface!
Step 4: Exploring Advanced Features
Once DeepSeek R1 is running, you can enhance its functionality:
??? Fine-Tuning – Customize the model with your dataset for specialized tasks.
?? API Integration – Connect DeepSeek R1 to third-party applications or chatbots.
?? Custom Prompts – Adjust prompt engineering techniques to get more accurate responses.
? Performance Optimization – Adjust batch size, caching, and GPU usage for smoother performance.
This Python code demonstrates how to interact with DeepSeek's API using the openai library to send prompts to a large language model (LLM) and receive responses. The following code, using Python as an example, demonstrates how to access the Chain of Thought (CoT) and the final answer, as well as how to conduct multi-round conversations:
from openai import OpenAI
client = OpenAI(api_key="<DeepSeek API Key>", base_url="https://api.deepseek.com")
# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
model="deepseek-reasoner",
messages=messages
)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
# Round 2
messages.append({'role': 'assistant', 'content': content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
model="deepseek-reasoner",
messages=messages
)
Can You Run ChatGPT Locally?
While DeepSeek R1 can be run locally, OpenAI's ChatGPT is currently cloud-based only. However, you can use open-source GPT models on your home or office computer, such as:
These models can be installed and run using similar methods outlined in this guide.
Conclusion
Running DeepSeek R1 locally on your computer empowers you with:
By following this guide, you can download, install, and run DeepSeek R1 with ChatbotAI, leveraging AI capabilities without cloud dependence.
While ChatGPT cannot be run locally, there are alternative open-source models available that provide a similar experience.
Suggested Additions & Enhancements
Can You Run ChatGPT Locally?
While the focus of this article is on DeepSeek's R1, it's worth addressing whether you can run ChatGPT locally. As of now, OpenAI's ChatGPT is primarily a cloud-based service, and the full model is not available for local deployment. However, there are smaller, open-source models inspired by GPT that you can run locally, such as GPT-Neo or GPT-J. These models can be downloaded and run using similar methods to those described above, though they may not offer the same level of performance or capabilities as ChatGPT.
The Benefits of Running a Local Model
Running a local model like DeepSeek's R1 or other open-source LLMs offers a wide range of benefits that make it an attractive alternative to cloud-based solutions. One of the most significant advantages is enhanced privacy and data security, as all data remains on your device, eliminating the risk of third-party access or exposure. This is particularly important for individuals and businesses handling sensitive information, as it ensures compliance with data protection regulations like GDPR or HIPAA. Additionally, running a model locally reduces issues of censorship, providing greater freedom of expression and unrestricted creativity, especially for users in regions with strict internet controls.
Another key benefit is offline accessibility, which allows you to use the model anywhere, even in environments with poor or no internet connectivity. This eliminates latency and ensures reliability, as you are not dependent on external servers. Over time, running a local model can also be more cost-effective, as it eliminates recurring subscription fees and allows you to optimize your hardware for specific needs. Furthermore, local deployment provides full control and customization, enabling you to fine-tune the model, adjust parameters, and integrate it into your existing workflows. This level of control is invaluable for developers, researchers, and businesses with specialized requirements.
Running a local model also offers independence from service providers, ensuring that you are not affected by policy changes, downtime, or discontinuation of services. For students and AI enthusiasts, it provides a hands-on learning experience and the opportunity to experiment with cutting-edge technology. Finally, by optimizing your hardware and software, you can minimize energy consumption and reduce your environmental impact, making local models a more sustainable option in some cases. Whether you prioritize privacy, control, or performance, running a local model empowers you to harness the full potential of large language models on your own terms.
Strategic Advisor | Helping European Companies Access US Markets | Host of The Exit Strategy Podcast
3 周I've been running my own local LLM using DeepSeek R1 on my computer for over two weeks now, and the experience has been eye-opening. While it runs slightly slower than cloud-based models like ChatGPT, the benefits—full control, enhanced privacy, and no external restrictions—are worth it. Running an LLM locally has given me a deeper understanding of AI deployment and the trade-offs between cloud and on-premise solutions. It’s fascinating to see how far open-source AI has come, and I highly recommend others explore the possibilities. ?? Have you tried running an LLM locally? What has your experience been like? Let’s discuss it! ??