Top 5 Local LLM Tools and Models in 2025

Running powerful AI language models locally has become increasingly accessible in 2025, offering privacy, cost savings, and full control over your data. As more developers and businesses seek alternatives to cloud-based AI services, local Large Language Models (LLMs) have evolved to provide impressive capabilities without requiring internet connectivity or subscription fees.

Why Run LLMs Locally in 2025?

The landscape of AI has evolved dramatically, but running LLMs locally continues to offer compelling advantages:

• Complete Data Privacy: Your prompts and data never leave your device
• No Subscription Costs: Use AI as much as you want without usage fees
• Offline Operation: Work without internet connectivity
• Customization Control: Fine-tune models for specific use cases
• Reduced Latency: Eliminate network delays for faster responses

Top 5 Local LLM Tools in 2025

1. Ollama

Ollama has emerged as the go-to solution for running LLMs locally, striking an ideal balance between ease of use and powerful features.

Key Features:

• One-line commands to pull and run models
• Support for 30+ optimized models including Llama 3, DeepSeek, and Phi-3
• Cross-platform support (Windows, macOS, Linux)
• OpenAI-compatible API
• Active community and regular updates

Getting Started with Ollama:

1. Install Ollama:

   • Visit ollama.com/download
   • Download and install for your operating system

2. Run a model:

   # Pull and run a model in one command
   ollama run qwen:0.5b
   
   # Or for smaller hardware:
   ollama run phi3:mini
   

   

3. Use the API:

   curl http://localhost:11434/api/chat -d '{
     "model": "qwen:0.5b",
     "messages": [
       {"role": "user", "content": "Explain quantum computing in simple terms"}
     ]
   }'
   

   

Best For: General users who want a straightforward way to run LLMs locally with minimal setup.

2. LM Studio

LM Studio provides the most polished graphical user interface for managing and running local LLMs, making it accessible for non-technical users.

Key Features:

• Intuitive GUI for model discovery and management
• Built-in chat interface with conversation history
• Advanced parameter tuning through visual controls
• Model performance comparison tools
• OpenAI-compatible API server

Getting Started with LM Studio:

1. Install LM Studio:

   • Visit lmstudio.ai
   • Download the installer for your OS

2. Download Models:

   • Navigate to the “Discover” tab
   • Browse and download models based on your hardware capabilities

3. Chat or Enable API:

   • Use the built-in chat interface
   • Or enable the API server through the “Developer” tab

Best For: Users who prefer graphical interfaces over command-line tools and want an all-in-one solution.

3. text-generation-webui

For those looking for a balance between powerful features and ease of installation, text-generation-webui offers a comprehensive solution with a web interface.

Key Features:

• Simple installation via pip or conda
• Intuitive web interface with chat and text completion modes
• Support for multiple model backends (GGUF, GPTQ, AWQ, etc.)
• Extensions ecosystem for added functionality
• Character creation and customization
• Built-in knowledge base/RAG capabilities

Getting Started with text-generation-webui:

1. Option 1: Portable builds (recommended):

   • Download from: GitHub Releases
   • No installation needed – just unzip and run
   • Compatible with GGUF (llama.cpp) models on Windows, Linux, and macOS

2. Launch the web UI:

   # Start the web interface
   text-generation-webui --listen
   

3. Download models through the interface:

   • Navigate to the “Models” tab in the web interface
   • Download models from Hugging Face directly through the UI
   • Select and load your preferred model

Best For: Users who want a feature-rich interface with easy installation and the flexibility to use various model formats.

4. GPT4All

GPT4All provides a polished desktop application experience with minimal setup required, making it ideal for Windows users.

Key Features:

• User-friendly desktop application
• Pre-configured with optimized models
• Built-in chat interface with conversation history
• Local RAG capabilities for document analysis
• Plugin ecosystem for extended functionality

Getting Started with GPT4All:

1. Install GPT4All:

   • Visit gpt4all.io
   • Download and install the desktop application

2. Select a model:

   • Use the built-in model downloader
   • Choose from various optimized models

3. Start chatting:

   • Use the intuitive chat interface
   • Adjust parameters through the settings panel

Best For: Windows users and those who prefer a traditional desktop application experience.

5. LocalAI

LocalAI offers the most versatile platform for developers who need to integrate local LLMs into their applications.

Key Features:

• Support for multiple model architectures (GGUF, ONNX, PyTorch)
• Drop-in replacement for OpenAI API
• Extensible plugin system
• Docker-ready deployment
• Multi-modal capabilities (text, image, audio)

Getting Started with LocalAI:

1. Using Docker:

   # CPU only image:
    docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-cpu
   
    # Nvidia GPU:
    docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
   
    # CPU and GPU image (bigger size):
    docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
   
    # AIO images (it will pre-download a set of models ready for use, see https://localai.io/basics/container/)
    docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-cpu
   

2. Download models:

   http://localhost:8080/browse/
   

Best For: Developers who need a flexible, API-compatible solution for integrating local LLMs into applications.

Bonus Tool: Jan

Jan is a comprehensive ChatGPT alternative that runs completely offline on your local device, offering full control and privacy.

Key Features:

• Powered by Cortex, a universal AI engine that runs on any hardware
• Model Library with popular LLMs like Llama, Gemma, Mistral, and Qwen
• OpenAI-compatible API server for integration with other applications
• Extensions system for customizing functionality
• Support for remote AI APIs like Groq and OpenRouter when needed

Getting Started with Jan:

1. Install Jan:

   • Visit jan.ai
   • Download the installer for your operating system (Windows, MacOS, or Linux)

2. Launch Jan and Download Models:

   • Open Jan after installation
   • Navigate to the Model Library
   • Choose from various optimized models based on your hardware capabilities

3. Start Using Jan:

   • Use the intuitive chat interface
   • Configure model parameters through settings
   • Optionally enable the API server for integration with other applications

Best For: Users looking for a polished, all-in-one solution that works across multiple platforms and hardware configurations.

Best Models for Local Deployment in 2025

The quality of locally runnable models has improved dramatically. Here are the standout models of 2025:

1. Llama 3 (8B and 70B)

Meta’s Llama 3 models offer an excellent balance of performance and efficiency:

• Llama 3 8B: Runs on mid-range hardware (16GB RAM)
• Llama 3 70B: Requires high-end hardware but offers near-commercial quality
• Strengths: General knowledge, reasoning, instruction following
• Compatible with: All 5 tools listed above plus Jan

2. Phi-3 Mini (4K)

Microsoft’s Phi-3 Mini provides impressive capabilities in a compact size:

• Runs on basic hardware (8GB RAM)
• Excellent performance for its size
• Strengths: Coding, logical reasoning, concise responses
• Compatible with: All 5 tools listed above plus Jan

3. DeepSeek Coder (7B)

Specialized for programming tasks with exceptional code generation:

• Requires mid-range hardware (16GB RAM)
• Strengths: Code generation, debugging, technical explanations
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan

4. Qwen2 (7B and 72B)

Alibaba’s Qwen2 models offer strong multilingual capabilities:

• Qwen2 7B: Runs on mid-range hardware
• Qwen2 72B: Requires high-end hardware
• Strengths: Multilingual support, creative writing, summarization
• Compatible with: Ollama, LM Studio, LocalAI, Jan

5. Mistral NeMo (8B)

Optimized for enterprise use cases with strong reasoning:

• Requires mid-range hardware (16GB RAM)
• Strengths: Business applications, document analysis, structured outputs
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan

Conclusion

The landscape of local LLM tools and models has matured significantly in 2025, offering viable alternatives to cloud-based AI services. Whether you prioritize ease of use (Ollama, GPT4All), graphical interfaces (LM Studio), flexibility (text-generation-webui), developer flexibility (LocalAI), or a comprehensive all-in-one solution (our bonus tool Jan), there’s a solution that fits your needs.

By running LLMs locally, you gain complete control over your data, eliminate subscription costs, and can operate entirely offline. As hardware continues to improve and models become more efficient, we can expect local AI capabilities to become even more accessible and powerful in the coming years.

Which local LLM tool are you using in 2025? Let us know in the comments below!