Top Local LLM Runtimes to Run Large Language Models Locally: A Hands-on Guide for Developers

Explore the best tools like Ollama, LM Studio, and GPT4All to run open-source LLMs privately and offline on your laptop — with features, setup guides, use cases, and real-world examples.

Large Language Models (LLMs) have transformed AI — but relying on cloud services for every interaction may raise concerns around privacy, cost, and latency.

The good news? Today’s tools let you run powerful LLMs entirely on your local machine — from chat assistants to code generators — even on a laptop!

This article will walk you through the top tools, their features, use cases, and how to get started step-by-step — with real examples.

Top Tools to Run LLMs Locally

Use Cases

• Private Chatbots – no cloud API needed.

• RAG Applications – fetch documents and summarize locally.

• Coding Assistants – pair with LLaMA or CodeLLaMA.

• Offline Research Agents – analyze PDFs, papers, and data.

• Fine-Tuning & LoRA Models – personalize models for tasks.

Key Features Comparison

Popular Local LLM Runtimes

1. LM Studio

What it is: GUI-based LLM runner with local model support.

Key Features:

• Easy-to-use desktop app (no command line needed).

• Download and run models like LLaMA, Mistral, GPT-J, etc.

• Uses ggml, gguf, or exllama format models.

• Integrated chat interface.

OS: macOS, Windows, Linux
Link: https://lmstudio.ai

How to Download a Model:

1. Open LM Studio app.

2. Go to the “Models” tab (left sidebar).

3. Search for a model (e.g., mistral, llama3).

4. Click “Download” next to your chosen model (prefer a .gguf format with quantization like Q4_K_M).

How to Load and Chat:

1. Go to the “Chat” tab.

2. Choose your downloaded model from the dropdown.

3. Type your prompt and press Enter.

   • Example: “Explain how neural networks work.”

2. Ollama

What it is: CLI and API-based local LLM runtime for running open-source models offline with advanced developer features.

Key Features:

• Command-line tool and OpenAI-compatible API for local inference

• Run models like LLaMA 3, Mistral, Gemma, Qwen, etc.

• Supports tool/function calling, Modelfiles, and LoRA adapters

• Uses efficient GGUF format for quantized models

• API-ready for integration into Python apps, LangChain, and RAG systems

• Privacy-focused: no internet needed after downloading models

OS: macOS, Windows, Linux
Link: https://ollama.com

How to Download a Model:

Open PowerShell or Terminal and run:

ollama pull llama3
ollama pull llama3.3
ollama run  llama3.3
ollama run  gemma3:1b
ollama list             # See installed models
ollama show llama3.3    # View model info
ollama pull gemma3
ollama pull mistral
ollama run mistral

Start chatting in the terminal:

>>> What is the capital of India?

How to Use in Python (API Access):

from langchain_ollama import OllamaLLM model = OllamaLLM(model="llama3") print(model.invoke("Summarize AI ethics."))

using OpenAI-compatible API:

import openai

openai.api_key = "ollama"
openai.base_url = "http://localhost:11434/v1"

response = openai.ChatCompletion.create(
    model="llama3",
    messages=[{"role": "user", "content": "What is machine learning?"}]
)
print(response['choices'][0]['message']['content'])

3. GPT4All

What it is: An open-source desktop app and CLI that enables local inference with open-source LLMs — ideal for privacy, offline use, and ease of setup.

Key Features:

• Easy-to-use desktop app with built-in chat interface

• Works fully offline after model download

• Supports popular GGUF models (e.g., Mistral, LLaMA, Falcon, Qwen)

• Also provides a local REST API for integration with custom apps

• Cross-platform: Windows, macOS, Linux

• Stores all data locally, ensuring data privacy

• Ideal for experimenting, chatting, summarizing, and offline research

OS: Windows, macOS, Linux
Link: https://www.nomic.ai/gpt4all

How to Download a Model:

1. Launch GPT4All Desktop App.

2. From the home screen, click “Download Models”.

3. Select a model (e.g., mistral-7b-instruct.gguf).

4. Click Download and wait for it to finish.

How to Start Chatting (GUI):

1. Choose your model in the left pane.

2. Start typing into the chat box.

   • Example: “Translate this to French: Hello, how are you?”

CLI (Optional):

From terminal:

./gpt4all-lora-quantized-win.exe --model ./models/mistral.gguf

Then interact in the terminal.

4. Text Generation WebUI

What it is: A powerful, web-based user interface to run a wide variety of LLMs locally using multiple backends (Transformers, GGUF, ExLlama, GPTQ).

Key Features:

• Web-based UI accessible in browser (localhost:7860)

• Supports multiple model formats: GGUF, Transformers, GPTQ, ExLlama

• Highly customizable: prompt templates, LoRA support, settings control

• Multi-user support, extensions (character AI, RAG, RP modules)

• Active open-source community

OS: Windows, macOS, Linux
Link: https://github.com/oobabooga/text-generation-webui

How to Download a Model:

1. Clone the repo:

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui

2. Run the installer (for Windows):

python download-model.py

3. Choose a model like

mistral-7b-instruct.Q4_K_M.gguf
llama2-13b-chat.Q5_K_S.gguf

How to Run and Chat:

Start the WebUI:

python server.py --model <your-model-path>

• Open browser and visit: http://localhost:7860

• Enter your prompt and start chatting.

5. llama.cpp

What it is: A low-level C++ library for running GGUF quantized models on CPUs and lightweight systems. Forms the backend for many local LLM tools like Ollama and LM Studio.

Key Features:

• Extremely fast, optimized for CPU usage

• Runs quantized models (GGUF)

• Works on Windows, Linux, macOS, and even Android & Raspberry Pi

• Used in headless, embedded, or CLI environments

• No external dependencies

OS: All OS (cross-platform)
Link: https://github.com/ggerganov/llama.cpp

How to Download & Run:

1. Clone and build:

git clone https://github.com/ggerganov/llama.cpp cd llama.cpp

2. Download GGUF model from Hugging Face (TheBloke):

• Place it in /models

3. Run a prompt:

./main -m models/llama3-8b.Q4_K_M.gguf -p "Write a haiku about summer."

No GUI – strictly CLI

Use for scripting, embedding, or building your own UI.

6. KoboldCpp

What it is: A fast, GPU-accelerated local LLM runner optimized for storytelling, roleplay, and creative writing, with GGUF model support and GUI.

Key Features:

• GUI interface focused on story generation

• Runs GGUF models (supports llama.cpp backend)

• Highly configurable character/author tools

• Used widely in text adventure and fan fiction communities

• Integrated memory, context settings, and style presets

OS: All OS
Link: https://github.com/LostRuins/koboldcpp

How to Use:

1. Download a GGUF model (e.g., mistral, llama2, etc.)

2. Run the koboldcpp.exe or equivalent script

3. GUI will launch in browser (or run as a local app)

Built-in GUI:

Offers text-box based storytelling UI with memory + persona config.

7. Faraday.dev

What it is: A developer-focused platform for building and testing private RAG pipelines and LLM backends locally with customizable UI and data connectors.

Key Features:

• Visual UI for building LLM+data pipelines

• Plug in your own GGUF/OpenAI-compatible models

• Connect local PDF/CSV/Markdown files for RAG

• Powerful for prototyping and testing AI workflows

• Works locally or inside Docker containers

OS: All OS (via Docker)
Link: https://faraday.dev

How to Use:

1. Install Docker

2. Run Faraday container:

docker run -it -p 3000:3000 faradayai/faraday

3. Open browser: http://localhost:3000

4. Load your model (e.g., Ollama, OpenRouter) and plug in documents

Visual Interface:

Drag-and-drop to create pipelines (e.g., Input → Embedder → Retriever → Model)

---

What Is GGUF Format?

If you're new to running large language models (LLMs) locally, you'll frequently encounter the term GGUF — and for good reason.

GGUF stands for "GPT-GGML Unified Format", and it's the standard model format used by most local LLM runtimes today — including Ollama, LM Studio, GPT4All, llama.cpp, and Text Generation WebUI.

Why GGUF Matters

Originally, LLMs like LLaMA, GPT-J, and Mistral were released in full-precision formats (FP16/FP32) that required powerful GPUs with 40+ GB of VRAM. Running these on personal machines was nearly impossible.

GGUF solves this by introducing:

• ✅ Quantization: Reduces model size without major performance loss

• ✅ Fast loading: Optimized for CPU and GPU inference

• ✅ Hardware-friendly: Can run on a typical laptop (even without GPU)

• ✅ Standardization: Works across all major local inference tools

GGUF vs Traditional Formats

Where to Find GGUF Models

The most popular source for high-quality, ready-to-use GGUF models is:

👉 Hugging Face – TheBloke’s Model Repository

You'll find quantized versions of:

• LLaMA 2 & LLaMA 3

• Mistral, Mixtral

• Gemma, Qwen, Phi-2

• WizardCoder, Dolphin, Orca

Example GGUF Model Names

• llama3-8b-instruct.Q4_K_M.gguf

• mistral-7b-instruct.Q5_K_M.gguf

• gemma-2b-it.Q4_K_M.gguf

Tip

If a tool says "load a GGUF model", it means:

• Download a .gguf file from Hugging Face

• Select a quantization level that fits your RAM (Q4_K_M is a good default)

• Load it in your tool of choice (Ollama, LM Studio, etc.)

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Recommendation for You

If you want:

• Beginner-friendly with chat UI → 🟢 LM Studio or GPT4All

• Advanced control + browser UI → 🟢 Text Generation WebUI

• Terminal + API + privacy → 🟢 Ollama

• Core library usage → 🟢 llama.cpp

• RAG and Dev Use → 🟢 Faraday.dev

Benefits of Running LLMs Locally

• Privacy-first: Your data stays on your machine.

• No API costs: Great for developers and hobbyists.

• Faster response times: No internet latency.

• Customizable: Tune with LoRA or adapters.

• Learning and experimenting: Explore the models deeply.

Summary

Final Thoughts

Running LLMs locally has never been more accessible.

Whether you're a developer building AI tools, a researcher experimenting with RAG, or just curious about LLMs — tools like Ollama, LM Studio, and GPT4All make it easy, free, and secure.

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