How to Run LLMs Locally in 2025 (PC & Laptop)

Key Takeaways

1️⃣Running LLMs locally means you can keep full control over your data security and privacy, while also ensuring fast responses in an offline environment.

2️⃣Currently, there are only three effective ways to run LLMs locally on PCs and laptops:

• Use official or community open-source models and set up a local runtime environment,
• Use the local AI platforms or applications powered by deep learning frameworks,
• Utilize pre-configured virtual environments or container-based solutions.

3️⃣If you prefer using DeepSeek AI Chat, follow these simple steps:

Step 1. Choose the LLM you want to run in the “Model” part, such as DeepSeek or Qwen.

Step 2. In “Install Path”, select the installation location, then click “Start Local Deployment”.

Step 3. Once installation is complete, the software will launch the AI Chat Tool, and you can start chatting with the AI.

Introduction: Why You Need to Run LLMs Locally?

When you send sensitive data to cloud-based LLMs, have you ever considered that this information might be passing through multiple servers? By now, the drawbacks of depending on cloud LLMs has exposed an increasing number of issues: risks of data breaches, service interruptions from unstable networks, slower response times, delays in response times, and the growing costs of API calls.

These limitations are driving more people to try running LLMs locally, aiming to use large models directly on their own PCs or laptops.

Whether you are a developer concerned about data security, an individual with daily needs, or an enterprise team looking to reduce AI usage expenses, you will find suitable solutions in the rest of the article.

What Are Local LLMs: Key Concepts and Benefits

Local LLMs are large language models that run directly on personal computers or laptops, independent of external cloud services. Unlike cloud-based models, these LLMs process all information locally and can run even on standard consumer hardware, thanks to open-weight models like DeepSeek, Qwen, and Gemma.

Naturally, many ask: “What are the benefits of running LLMs locally?”

It’s important to note that local LLMs are not meant to completely replace cloud-based models, but serve as a complementary solution. Running models locally allows users to a) Keep sensitive information entirely on the local device, eliminating the risk of third-party access; b) Enjoy stable performance and near-instant responses even without an internet connection; c) Avoid recurring API fees, which is ideal for frequent users; d) Adjust model parameters and build personal knowledge bases to meet individualized AI needs.

Preparing Your PC/Laptop for Local LLM Deployment

Proper hardware and software preparation is crucial before deploying LLMs locally, as it directly affects model loading speed and runtime stability. Below is a deployment checklist verified through practical testing:

Component	Minimum Configuration (1.5B–7B models)	Recommended Configuration (14B+ models)	Key Role
CPU/NPU	Intel i5-11400H or AMD Ryzen 5	Intel Core Ultra 7 258V (115 TOPS AI) / AMD Ryzen AI 9365 (73 TOPS)	NPU performance directly impacts local inference speed
GPU	NVIDIA GTX 1050 Ti (4GB VRAM)	NVIDIA RTX 4070 (8GB GDDR6) or Mac M2 chip	GPU acceleration can boost response speed by 3–5x
RAM	8GB RAM (4-bit quantized 7B models)	32GB LPDDR5X (bandwidth ≥ 256GB/s)	High-bandwidth memory reduces data transfer bottlenecks
Storage	256GB SATA SSD	1TB PCIe 5.0 NVMe SSD (e.g., Samsung 990 Pro)	NVMe SSDs can reduce model loading time by ~50% compared to traditional HDDs

Software Environment Preparation:

• GPU Drivers: NVIDIA users should install CUDA Toolkit 12.4+; AMD users should update to Radeon Software 24.7.1+.
• Model Deployment Tools: Recommended to use DeepSeek AI Chat for one-click deployment, which installs the required models directly on your local device.
• System Optimization: Close background programs that consume resources to ensure stable model operation.

Step-by-Step Methods to Run LLMs Locally (PC/Laptop)

If you want to run LLMs efficiently on your PC or laptop, DeepSeek AI Chat is your best choice. It allows you to deploy various open-source AI models on your PC, and you can use AI capabilities without depending on the cloud. With this local LLMs deployment tool, you can quickly launch LLMs locally for text generation, analysis, or development tasks.

Why DeepSeek AI Chat stands out from other local LLM deployment methods:

• Supports direct local deployment of multiple large models
• Provides one-click deployment, lowering the hardware barrier
• Features an intuitive and user-friendly interface for easy operation
• Supports multiple model parameter adjustments and agent usage
• Eliminates ongoing cloud API fees and avoids interruptions caused by network instability

To get the fastest deployment, follow the following steps to use DeepSeek AI Chat：

Step 1. Launch DeepSeek AI Chat, select the model you want to use, such as DeepSeek or Qwen, and specify the installation path for the model.

Step 2. Click “Start Local Deployment”, and the tool will automatically handle model downloading, quantization, and environment setup.

Step 3. Once installation is complete, DeepSeek AI Chat will launch the AI Chat tool, allowing you to interact with the model locally.

Disclaimer: DeepSeek AI Chat does not support unauthorized commercial use or any operations that violate privacy. Before using, ensure that your actions comply with applicable laws and respect data ownership.

Alternative Local LLM Tools: Ollama, LM Studio, and More

Beyond DeepSeek AI Chat, two leading tools dominate local LLM deployment:

Tool	Advantages	Limitations	Suitable Users
Ollama	Lightweight CLI / API support / GPU utilization up to 90%+	No native GUI	Developers / Users needing script integration
LM Studio	Graphical interface / Model management / Vulkan acceleration	~10% performance loss on Windows	Beginners / Non-technical users
llama.cpp	Highly optimized performance / Cross-platform compatibility	Requires compilation / Complex setup	Hardware enthusiasts / Performance seekers

FAQs on Running LLMs on Your PC or Laptop

Q1: Is it legal to run LLMs locally?

Running LLMs locally requires compliance with the model’s license and applicable laws. Only deploy models that you have legal rights to use, are covered by open-source licenses, or are explicitly authorized for personal use.

Q2: Do local LLMs have high hardware requirements?

Hardware requirements vary depending on the model. Smaller models (1.5B–7B parameters) can run on standard consumer-grade computers, while larger models (14B+ parameters) require high-performance hardware.

Q3: Can local LLMs work offline?

Yes, LLMs running locally process all data on your device, allowing you to perform text generation, analysis, or image processing without needing an internet connection.