Ollama vs PrivateGPT – Choosing Your Best Local LLMs in 2025
- Philip Moses
- Jul 11
- 4 min read
In 2025, artificial intelligence is changing. It's not just about cloud-based solutions anymore; it's becoming more personal and private, right on your own computer. As people and businesses want more control over their data and privacy, local Large Language Models (LLMs) are stepping into the spotlight.
In this blog, we'll compare two leading local LLM solutions: Ollama and PrivateGPT. We'll look at what they offer, how they differ, and help you figure out which one is the best fit for your needs.
In the next sections, we'll explore why local LLMs are becoming so important in 2025, followed by a detailed comparison of Ollama and PrivateGPT to see how they stack up against each other.
Why Local LLMs Are Gaining Importance in 2025
More individuals and businesses are focusing on AI privacy and cost control. Instead of sending data to the cloud, local LLMs process it right on your computer or server. Here are some benefits:
Enhanced Privacy and Security: Your data stays on your machine, reducing exposure to external threats.
Cost Efficiency: No ongoing fees for API usage, making it economical in the long run.
Faster Processing: Without the need for internet connectivity, responses are quicker.
Full Control: You decide how and where your AI operates, giving you complete autonomy.
These advantages have led to the rise of powerful tools like Ollama and PrivateGPT.
Ollama: The Versatile Local LLM Engine
Ollama has quickly become a favorite for running AI models locally. It's lightweight, open-source, and works on macOS, Windows, and Linux.
Key Features of Ollama
Easy to Install and Use: You don't need to be a tech expert. Ollama can be set up with simple commands, allowing you to run models in minutes.
Extensive Model Library: Ollama supports a wide range of open-source models, including Llama 2, Llama 3, Llama 4, Google’s Gemma, Mistral, Vicuna, DeepSeek, IBM Granite 3.0, and many others in GGUF/GGML formats.
Custom Modelfiles: These are like "recipes" for setting up AI models. You can fine-tune how a model behaves, adjust settings, and create specialized versions for your tasks.
Multimodal Capabilities: Ollama can handle both text and images. It can answer questions about images, read charts or documents, and perform OCR (text recognition in images).
Tool Calling & APIs: It connects with external tools and APIs, making it great for developers who want to integrate AI features into their software.
Offline AI Power: All processing happens locally, keeping your data private and secure.
Hardware Requirements for Ollama
Running large AI models on your computer requires substantial hardware:
RAM: 16-64GB recommended
Storage: Fast NVMe SSD
GPU: Needed for larger models (e.g., 8GB VRAM for 13B models)
While smaller models can run on a decent CPU, larger ones require a strong GPU for optimal performance.
PrivateGPT: The Secure Fortress for Private Documents
PrivateGPT is designed with privacy in mind. It's ideal for businesses and users who need to keep sensitive documents secure and offline.
Key Features of PrivateGPT
Privacy-First Design: All AI processing happens within your own machine. Nothing is sent to the cloud, which is crucial for handling legal documents, business secrets, health data, and financial reports.
Document Q&A: PrivateGPT can read and understand large sets of documents, answer questions based on your data, summarize lengthy reports, and help find specific information quickly.
Local Vector Database: It stores and indexes your documents locally. When you ask a question, it searches your own knowledge base to find answers.
Customizable for Business: PrivateGPT can be tailored to different document types, specialized prompts, and internal business workflows.
Offline Operation: Once installed, it doesn’t need internet access.
Hardware Requirements for PrivateGPT
PrivateGPT also demands solid hardware:
RAM: 16GB minimum, 32GB recommended
CPU: Modern multi-core CPU
GPU: 8GB VRAM or more for smoother operation
Storage: ~100GB free space
Its local search features and advanced encryption make it resource-intensive.
Ollama vs PrivateGPT : The 2025 Comparison
Here’s how they compare side by side:
Feature | Ollama | PrivateGPT |
| Versatile AI runner | Private document Q&A |
| Very easy, fast install | Moderate, more technical |
| Data stays local | Data stays local + encrypted |
| Yes | Yes |
| Text + images | Mainly text-focused |
| Modelfiles, LoRA | Custom data chunking, workflows |
| Yes | Yes (enterprise-focused) |
| Developers, enthusiasts, AI experiments | Businesses, compliance-heavy industries |
When to Choose Ollama
You’re a developer wanting to test different AI models locally.
You want to build apps that integrate AI features.
You prefer quick installs and easy experiments.
You care about privacy but don’t have strict compliance needs.
When to Choose PrivateGPT
You handle highly sensitive data like contracts, patient records, or financials.
You want AI to read and search your internal documents.
You’re in a regulated industry (e.g., healthcare, legal, finance).
You’re okay investing time in setup for top-level privacy.
Can You Use Both Together?
Absolutely! Many businesses might use Ollama as the engine to run local AI models and pair it with PrivateGPT for secure document handling. This combination offers the flexibility and wide model support of Ollama with the robust data privacy of PrivateGPT.
Conclusion: The Future Is Local and Private
In 2025, AI is becoming more personal and private. Tools like Ollama and PrivateGPT are leading the way by putting powerful AI directly on your own machine, giving you control over your data and costs.
Ollama is your go-to for experimenting with a variety of AI models and building new apps, while PrivateGPT is your choice for handling private documents securely and offline. Whichever you choose, one thing is clear: the future of AI is local, private, and tailored to your needs.