Web UI Options for Running Local LLMs

Introduction

 

Running Large Language Models (LLMs) locally provides important advantages, particularly when privacy and data control are priorities. Instead of sending sensitive information to external AI services, local models allow you to work directly on your own system.

 

However, the experience of using a local LLM depends heavily on the Web User Interface (Web UI) you choose. A good Web UI makes interacting with models easier, supports document analysis, and improves overall workflow efficiency.

 

This page reviews several Web UI options that can be used with locally installed LLMs. The comparison focuses on factors such as:

 

• ease of setup
• responsiveness
• document support
• accuracy during testing
• suitability for business or personal use

 

These observations are based on practical testing carried out while evaluating tools for private, local AI workflows.

 

The following Web UI tools were tested as part of exploring practical ways to run local LLM systems while maintaining privacy and control.

Oobabooga Text-Generation-Webui

oobabooga’s text-generation-webui is a widely known open-source interface for running language models locally. It supports many different models and offers extensive customization options.

During testing, the interface proved flexible but less reliable for document-focused tasks. Performance when processing text inputs was slower than expected, and the workflow felt more suited to experimentation and character-based chat environments than structured business applications.

While it remains a powerful open-source project with an active community, it may require additional configuration and experimentation to achieve stable results for practical use cases.

License: Open-source.

PrivateGPT

PrivateGPT is another open-source project designed to allow users to interact with their own documents privately using local language models.

In testing, the web interface showed instability and performance issues when processing document queries. While the project can work more reliably when run inside development environments such as Visual Studio Code, this additional setup can make it less convenient for users looking for a simple web-based interface.

Accuracy when analysing uploaded documents was inconsistent during testing, and response times were slower than expected for everyday use.

License: Open-source.

LocalGPT

LocalGPT provides functionality similar to PrivateGPT but with broader support for document formats. It can process files such as:

• PDF
• DOCX
• XLSX
• TXT
• HTML

This expanded file compatibility makes it more flexible for document-based workflows.

However, while response times were slightly better during testing compared to PrivateGPT, overall accuracy and reliability were still not strong enough for consistent practical use in business environments.

License: Open-source.

H2O.ai

H2O.ai offers a web-based interface that allows users to interact with language models and perform document-based analysis.

While the platform demonstrated stronger performance during testing than some other options, it often requires interaction with external servers for certain features. For users working with sensitive information, this reliance on external services may raise privacy considerations.

For environments where strict local data control is required, this architecture may not be ideal.

License: Restricted open-source.

Ollama

Ollama has emerged as one of the most promising tools for running local language models in a streamlined way. It focuses on simplicity and provides an efficient method for downloading and running models locally.

When used within a Linux environment or through Windows Subsystem for Linux (WSL), Ollama offers a straightforward workflow and strong community support. Its design makes it easier to experiment with different models while maintaining local control over data.

While further testing is always recommended for specific workflows, Ollama currently stands out as one of the most practical options for local LLM experimentation and development.

License: Apache License 2.0.

Quick Summary

Best for experimentation: oobabooga
Best document workflow: LocalGPT
Best overall direction: Ollama
Most restrictive for privacy: H2O.ai

Final Thoughts

Choosing the right Web UI for local LLM use depends on your priorities. Important factors include:

• stability and responsiveness
• document support
• privacy and data control
• ease of setup
• compatibility with different models

Many open-source tools continue to evolve rapidly, and new improvements appear frequently. While some current options still feel experimental, others—such as Ollama—are moving toward more practical and reliable local AI workflows.

If your goal is to explore private AI systems while maintaining control over your data, experimenting with different Web UI tools is an excellent way to understand how local LLM environments work.

Note:

Local AI tools develop quickly. Features, compatibility, and performance may change as projects continue to evolve.