To use TT-Studio's deployment features, you need access to a Tenstorrent AI accelerator.
Alternatively, you can connect to remote endpoints running models on Tenstorrent cards without local hardware.

TL;DR: TT-Studio is an easy-to-use web interface for running AI models on Tenstorrent hardware. It handles all the technical setup automatically and gives you a simple GUI to deploy models, chat with models, and more.

TT-Studio combines TT Inference Server's core packaging setup, containerization, and deployment automation with TT-Metal's model execution framework specifically optimized for Tenstorrent hardware and provides an intuitive GUI for model management and interaction.

Before you start, make sure you have:

⚠️ IMPORTANT: Complete the base Tenstorrent software installation first:

Follow the Tenstorrent Getting Started Guide

This guide covers hardware setup, driver installation, and system configuration. You must complete this before using TT-Studio.

Also ensure you have:

• Python 3.8+ (Download here)
• Docker (Installation guide)

Want to start using AI models right away on your Tenstorrent hardware? This is for you!

Quick Setup:

git clone https://github.com/tenstorrent/tt-studio.git && cd tt-studio && python3 run.py

What happens step by step:

1. Downloads TT-Studio - Gets the code from GitHub
2. Enters the directory - Changes to the tt-studio folder
3. Runs the setup script - Automatically configures everything
4. Initializes submodules - Downloads TT Inference Server and dependencies
5. Prompts for configuration - Asks for your Hugging Face token and generates security keys
6. Builds containers - Sets up Docker environments for frontend and backend
7. Starts all services - Launches the web interface and backend server

After Setup:

• Go to http://localhost:3000 to use TT-Studio
• The backend runs at http://localhost:8001
• Individual AI models run on ports 7000+ (e.g., 7001, 7002, etc.)

To Stop TT-Studio:

python3 run.py --cleanup

Note: This command will stop and remove all running Docker containers, including any currently deployed models. It cleans up containers and networks but preserves your data and configuration files.

🎯 What Can You Do Next?

Once TT-Studio is running:

1. Deploy a Model - Go to the Model Deployment page and deploy a model to start using AI features
2. Use AI Features:
   • 💬 Chat with AI models - Upload documents and ask questions
   • 🖼️ Generate images - Create art with Stable Diffusion
   • 🎤 Process speech - Convert speech to text with Whisper
   • 👁️ Analyze images - Detect objects with YOLO models
   • 📚 RAG (Retrieval-Augmented Generation) - Query your documents with AI-powered search
   • 🤖 AI Agent - Autonomous AI assistant for complex tasks

📖 Learn More: Check out our Model Interface Guide for detailed tutorials.

🆘 Need Help?

• No Tenstorrent hardware? → Remote Endpoint Setup - Connect to remote Tenstorrent cards
• Issues during setup? → Troubleshooting Guide
• Questions? → FAQ
• Remote server setup? → See Remote Access Guide below
• Technical support? → Submit issues on GitHub

Want to contribute to TT-Studio or modify it?

Development Mode Setup:

git clone https://github.com/tenstorrent/tt-studio.git
cd tt-studio
python3 run.py --dev

Development Features:

• Hot Reload: Code changes automatically trigger rebuilds
• Container Mounting: Local files mounted for real-time development
• Automatic Setup: All submodules and dependencies handled automatically

Get Started:

• Contributing Guide - How to contribute code
• Development Setup - Set up your dev environment
• Frontend Development - React frontend
• Backend API - Django backend

Resources:

• Development Tools
• Complete run.py Guide
• vLLM Models Guide

Running TT-Studio on a remote server? Use SSH port forwarding to access it from your local browser:

ssh -L 3000:localhost:3000 -L 8001:localhost:8001 -L 7000-7010:localhost:7000-7010 username@your-server

Note: Port range 7000-7010 forwards the model inference ports where individual AI models run.

Then open http://localhost:3000 in your local browser.

Hardware Requirements: Tenstorrent AI accelerator hardware is automatically detected when available. You can also connect to remote endpoints if you don't have direct hardware access.

TT-Studio combines TT Inference Server and TT-Metal to provide:

• Modern Web Interface: React-based UI for easy model interaction
• Django Backend: Robust backend service for model management and deployment
• Vector Database: ChromaDB for document storage and semantic search
• Multiple AI Models: Chat, vision, speech, and image generation
• Model Isolation: Each AI model runs on separate ports (7000+) for better resource management
• Hardware Optimization: Specifically optimized for Tenstorrent devices
• Docker Containers: Isolated environments for frontend, backend, and inference services

• Language Models (LLMs): Chat, Q&A, text completion
• Computer Vision: Object detection with YOLO
• Speech Processing: Speech-to-text with Whisper
• Image Generation: Create images with Stable Diffusion

Want to contribute or customize TT-Studio?

Get Started:

• Contributing Guide - How to contribute code
• Development Setup - Set up your dev environment
• Frontend Development - React frontend
• Backend API - Django backend

Development Mode:

python3 run.py --dev  # Enables hot reload for development

Development Features:

• Hot Reload: Code changes automatically trigger rebuilds
• Container Mounting: Local files mounted for real-time development
• Automatic Setup: All submodules and dependencies handled automatically

Resources:

• Development Tools
• Complete run.py Guide
• vLLM Models Guide

• FAQ - Quick answers to common questions
• Troubleshooting Guide - Fix common setup issues
• Model Interface Guide - Detailed tutorials for using AI models
• Complete run.py Guide - Advanced usage and command-line options

• Having issues? Check our Troubleshooting Guide
• Want to contribute? See our Contributing Guide
• Need specific models? Follow our vLLM Models Guide

⚠️ Note: The startup.sh script is deprecated. Always use python3 run.py for setup and management.