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. With groundbreaking releases like OpenAI’s GPT-OSS, DeepSeek V3.2-Exp, Qwen3-Next/Omni, Qwen3-Coder-480B for agentic coding, Meta’s Llama 4, and Google’s Gemma 3, local LLMs now rival cloud-based services in performance while maintaining complete data privacy and eliminating subscription costs.

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 100+ optimized models including GPT-OSS, DeepSeek V3.2-Exp, Qwen3-Next/Omni/Coder, VaultGemma, and Llama 4
• 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 the latest models in one command
   ollama run qwen3:0.6b
   
   # For smaller hardware:
   ollama run gemma3:1b
   
   # For the latest reasoning models:
   ollama run deepseek-v3.2-exp:7b
   
   # For the most advanced open model:
   ollama run llama4:8b
   

   

3. Use the API:

   curl http://localhost:11434/api/chat -d '{
     "model": "llama4:8b",
     "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.

Related: Learn how to run Ollama on Google Colab or share your Ollama API online for remote access.

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.

Related: Check out our detailed LM Studio guide for step-by-step setup instructions and advanced features.

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.

Related: Learn how to self-host Jan as an AI assistant and make it accessible from anywhere.

Best Models for Local Deployment in 2025

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

1. GPT-OSS (20B and 120B)

OpenAI’s groundbreaking first open-weight models represent a major shift in the AI landscape, bringing enterprise-grade reasoning capabilities to local deployment. These models excel at advanced reasoning, sophisticated tool calling, and complex agentic workflows, making them ideal for developers building AI applications that require reliable decision-making capabilities.

• Release Date: August 2025
• Official Website: OpenAI
• Models:
  • GPT-OSS 20B - Runs on high-end consumer hardware (32GB+ RAM)
  • GPT-OSS 120B - Requires enterprise-grade infrastructure
• Strengths: Advanced reasoning, tool calling, agentic workflows, GPT-4 level performance
• Compatible with: Ollama, LM Studio, LocalAI

2. DeepSeek V3.2-Exp

The latest evolution in DeepSeek’s reasoning model family represents cutting-edge advancement in AI reasoning capabilities, approaching the performance levels of O3 and Gemini 2.5 Pro. This experimental model showcases DeepSeek’s continued innovation in mathematical problem solving and complex reasoning tasks. The model features an advanced “thinking mode” that allows it to work through problems step-by-step, making it particularly valuable for developers working on applications requiring logical reasoning, code analysis, and mathematical computations.

• Release Date: September 2025
• Official Website: DeepSeek
• Model: DeepSeek V3.2-Exp
• Hardware Requirements: 16GB RAM (smaller variants) to 64GB+ RAM (larger configurations)
• Strengths: Advanced reasoning, thinking mode, mathematical problem solving, code analysis
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan

3. Qwen3-Next and Qwen3-Omni

Alibaba’s latest innovations in the Qwen family introduce two specialized variants that push the boundaries of multimodal AI. Qwen3-Next represents the next generation of dense and mixture-of-experts (MoE) models, offering unprecedented multilingual capabilities and advanced reasoning across 128K token contexts. Meanwhile, Qwen3-Omni breaks new ground as a truly multimodal model that seamlessly handles text, images, audio, and video inputs, making it ideal for developers building comprehensive AI applications.

• Release Date: September 2025
• Official Website: Qwen
• Models:
  • Qwen3-Next - Next-gen dense and MoE models
  • Qwen3-Omni - Multimodal (text, images, audio, video)
• Hardware Requirements: 16GB RAM (8B variants) to 32GB+ RAM (larger configurations)
• Strengths: Multilingual excellence, tool calling, thinking capabilities, 128K context, multimodal understanding
• Compatible with: Ollama, LM Studio, LocalAI, Jan

4. Gemma 3 Family

Google’s Gemma 3 family has expanded significantly with multiple specialized variants representing the newest evolution in Google’s open-source AI initiative. These models are designed with safety and efficiency at their core, offering developers reliable performance across various hardware configurations. All Gemma 3 models feature advanced vision understanding capabilities and maintain Google’s commitment to responsible AI development.

• Release Dates: August - September 2025
• Official Website: Google Gemma
• Models:
  • Gemma 3 270M (August 2025) - Ultra-compact model
  • EmbeddingGemma 308M (September 2025) - Specialized embeddings
  • VaultGemma 1B (September 2025) - Latest flagship compact model
  • Gemma 3 4B - Runs on basic hardware (8GB RAM)
  • Gemma 3 27B - High-end consumer hardware (32GB RAM)
• Strengths: Vision understanding, efficient performance, safety-focused design
• License: Open-source weights (review Google’s Gemma license for usage terms)
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan

5. Llama 4

Meta’s revolutionary Llama 4 represents a quantum leap in open-source language model capabilities as the company’s most advanced offering to date. This latest iteration builds upon the success of the Llama 3 series while introducing significant architectural improvements that deliver enhanced reasoning, superior instruction following, and remarkable efficiency gains. Llama 4 demonstrates Meta’s continued commitment to democratizing AI by providing developers with a model that rivals proprietary alternatives while maintaining complete transparency and control.

• Release Date: April 2025
• Official Website: Meta Llama
• Model: Llama 4 (Multiple variants available)
• Hardware Requirements: 64GB+ RAM for optimal performance
• Strengths: General knowledge, creative writing, complex reasoning, code generation, efficiency improvements
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan

6. Qwen3-Coder-480B-A35B-Instruct

The best Qwen model for coding, particularly excelling at agentic coding and tasks requiring large context windows. This flagship coding model represents Alibaba’s most advanced offering for software development, delivering unprecedented capabilities in complex programming tasks. The model is specifically optimized for agentic coding workflows where AI systems need to understand, plan, and execute multi-step coding projects autonomously. With its massive context window, it can handle entire codebases and maintain context across extensive development sessions.

• Release Date: October 2025
• Official Website: Qwen
• Model: Qwen3-Coder-480B-A35B-Instruct (480B parameters, 35B active)
• Hardware Requirements: Enterprise-grade hardware (128GB+ RAM recommended)
• Strengths: Agentic coding, large context windows, complex project understanding, autonomous development workflows, multi-step planning
• Best For: Large-scale software projects, codebase analysis, automated refactoring, complex debugging
• Compatible with: Ollama, LM Studio, text-generation-webui, Jan (with sufficient hardware)

Related: Want to run DeepSeek models specifically? Check out our guide on running DeepSeek locally.

Conclusion

Local LLMs have evolved rapidly in 2025, with models like GPT-OSS, DeepSeek V3.2-Exp, Qwen3-Omni/Coder, Llama 4, and VaultGemma bringing near-commercial AI performance to personal devices.

Whether you prefer simplicity (Ollama, GPT4All), GUIs (LM Studio), flexibility (text-generation-webui, LocalAI), or all-in-one solutions (Jan), there’s a perfect fit for every user.

These new models deliver powerful reasoning, multimodal support, agentic coding capabilities, and built-in tool-calling—making local AI both capable and secure. Running LLMs locally gives you full data control, no subscription costs, and offline functionality.

Want more? Explore our guide on best AI tools for coding to boost your workflow.