The Best Tools for Running LLMs Locally in 2025

Why Run LLMs Locally?

Cloud-based AI APIs are convenient, but they come with tradeoffs: usage costs, rate limits, data privacy concerns, and internet dependency. Running a large language model locally on your own machine gives you full control, zero per-query costs, and complete data privacy. The hardware requirements have also dropped significantly — many capable models now run on a modern laptop.

What You Need to Get Started

RAM: At minimum 8GB for small models (7B parameters); 16–32GB for larger ones.
GPU (optional but recommended): NVIDIA GPUs with CUDA support dramatically accelerate inference. Apple Silicon Macs use Metal and perform surprisingly well.
Disk space: Models range from ~4GB (quantized 7B) to 40GB+ (larger variants).

Top Tools for Local LLM Development

1. Ollama

Best for: Beginners and quick local setup

Ollama is arguably the easiest way to get a model running locally. A single CLI command pulls and runs models like Llama 3, Mistral, and Gemma. It exposes a local REST API compatible with OpenAI's API format, making it easy to plug into existing tools.

Runs on macOS, Linux, and Windows
Native Apple Silicon support
Simple model library: ollama run llama3
Open source (MIT license)

2. LM Studio

Best for: GUI-first users and model browsing

LM Studio provides a polished desktop interface for downloading, managing, and chatting with local models. It includes a built-in model browser sourcing from Hugging Face, a chat UI, and a local server mode. Ideal if you want a visual experience without touching the terminal.

3. llama.cpp

Best for: Performance, customization, and CPU inference

The foundational C++ library that most other tools build on. llama.cpp enables highly optimized inference on CPU and GPU. It's the right choice if you're building a custom pipeline, need maximum performance control, or want to understand what's happening under the hood.

4. Jan

Best for: Privacy-first all-in-one desktop app

Jan is an open-source ChatGPT alternative that runs 100% offline. It supports multiple model backends, has an extension ecosystem, and positions itself explicitly around privacy — no telemetry, no cloud dependency.

5. Open WebUI

Best for: Teams and self-hosted deployments

Open WebUI (formerly Ollama WebUI) is a feature-rich web interface for Ollama or any OpenAI-compatible backend. It supports multi-user setups, conversation history, RAG (retrieval-augmented generation), and model switching — all self-hosted.

Recommended Models to Try

Model	Size	Strengths
Llama 3.1 8B	~5GB (Q4)	General purpose, strong reasoning
Mistral 7B	~4GB (Q4)	Fast, efficient, good for coding
Phi-3 Mini	~2GB	Tiny but capable, great for edge
Gemma 2 9B	~6GB (Q4)	Strong instruction following
DeepSeek Coder	~4–7GB	Excellent for code generation

Getting Started in 5 Minutes

Install Ollama from ollama.com
Run ollama pull mistral in your terminal
Run ollama run mistral to open a chat session
Or send requests to http://localhost:11434/api/generate from your app

Local LLMs have crossed the threshold from research curiosity to practical developer tool. Whether you're building a private chatbot, experimenting with fine-tuning, or just avoiding cloud costs, these tools make it accessible.