# Tt Studio Overview

> Project map for TT-Studio — what the platform is, where its docs live, its key components (React frontend, Django backend, FastAPI inference server, Docker), and the AI model types it supports. Use when you need a high-level orientation of the repo, want to know which doc covers a topic, or are trying to locate where a component or capability lives before diving into code.

- Skill: `tenstorrent/tt-studio-overview` (Agent Skill)
- Install (CLI): `npx skillmds@latest add tenstorrent/tt-studio-overview`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tenstorrent/tt-studio-overview/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: tenstorrent (https://skillmd.com/u/tenstorrent)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tenstorrent/tt-studio-overview

---


# TT-Studio

> TT-Studio is an easy-to-use web interface for running AI models on Tenstorrent hardware.
  It combines TT Inference Server's core packaging setup, containerization,
  and deployment automation with TT-Metal's model execution framework specifically optimized for Tenstorrent hardware.

Important notes:

- TT-Studio requires access to a Tenstorrent AI accelerator for full deployment features
- Alternatively, you can connect just the frontend to a remote API endpoint without direct hardware access
- The platform provides automatic hardware detection and seamless integration with Tenstorrent devices
- Uses containerized deployment through Docker for isolation and easy deployment
- The startup.sh script is deprecated - use `python run.py` for all operations

## Docs

- [Main README](README.md): Complete overview, setup instructions, and quick start guide
- [Setup Guide](dev-docs/run-py-guide.md): Complete installation & configuration using run.py
- [FAQ](dev-docs/FAQ.md): Quick answers to common questions about TT-Studio
- [Model Interface Guide](dev-docs/model-interface.md): Using TT-Studio as AI playground (Chat, Vision, Speech, Images)
- [Troubleshooting Guide](dev-docs/troubleshooting.md): Solutions for common setup and runtime issues
- [Contributing Guide](CONTRIBUTING.md): How to contribute code to the project
- [Development Setup](dev-docs/development.md): Development environment configuration

## Examples

- [AI Model Interface](dev-docs/model-interface.md): Complete examples of using Chat, Vision, Speech, and Image models
- [vLLM Models Guide](dev-docs/HowToRun_vLLM_Models.md): Specific examples for running vLLM models
- [AI Agent Setup](app/agent/README.md): Setting up and using the AI assistant functionality

## Key Components

- **Frontend Interface**: Modern React-based UI for model interaction and management
- **Backend API**: Django-based service for model management, deployment, and API endpoints
- **TT Inference Server**: FastAPI server for handling model inference requests
- **Docker Containers**: Complete containerization for isolation and easy deployment
- **Automatic Hardware Detection**: Seamless integration and auto-mounting of Tenstorrent devices (/dev/tenstorrent)
- **Automated Setup**: Complete environment configuration and model setup automation via run.py script

## Supported AI Models

- **Chat-based Language Models (LLMs)**: Text generation and conversational AI
- **Computer Vision (YOLO)**: Object detection and image analysis
- **Speech Recognition (Whisper)**: Audio-to-text transcription
- **Image Generation (Stable Diffusion)**: AI-powered image creation