tt-studio-overview

name	tt-studio-overview
description	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.

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: Complete overview, setup instructions, and quick start guide
• Setup Guide: Complete installation & configuration using run.py
• FAQ: Quick answers to common questions about TT-Studio
• Model Interface Guide: Using TT-Studio as AI playground (Chat, Vision, Speech, Images)
• Troubleshooting Guide: Solutions for common setup and runtime issues
• Contributing Guide: How to contribute code to the project
• Development Setup: Development environment configuration

Examples

• AI Model Interface: Complete examples of using Chat, Vision, Speech, and Image models
• vLLM Models Guide: Specific examples for running vLLM models
• AI Agent Setup: 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