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Get started with AI inference and custom training on Tenstorrent hardware.
This extension provides interactive walkthroughs to help you set up your development environment, run inference on Tenstorrent devices, train custom models, and build production AI applications.
Already using Tenstorrent Cloud, Quietbox, or a preinstalled system? Your environment is ready - skip to the Learning Path below!
If you need to set up a fresh system, use TT-Installer 2.0 for one-command installation:
Quick Install:
/bin/bash -c "$(curl -fsSL https://github.com/tenstorrent/tt-installer/releases/latest/download/install.sh)"
📖 Run Quick Install | 📥 Download Script | 📚 Full Documentation
TT-Installer sets up drivers, firmware, tt-smi, HugePages, and TT-Metalium™ containers in 5-15 minutes.
Click on any lesson below to open it in the new interactive lesson viewer. Or click "Open Lessons Sidebar" above to browse all lessons in the sidebar.
Lessons are organized by category - start with "Your First Inference" for the quickest path to running models, or jump to "Custom Training" to train your own!
Scan for connected Tenstorrent devices and verify they're properly recognized by the system.
Test your TT-Metalium installation by running a sample operation on your Tenstorrent device.
Download the Llama-3.1-8B-Instruct model and run inference on your Tenstorrent hardware.
Build a custom chat application using TT-Metalium's Generator API directly.
Create a production-ready Flask API with the model loaded in memory.
Use Tenstorrent's official inference server for production deployments with simple CLI configuration.
Deploy with vLLM - OpenAI-compatible APIs, continuous batching, and enterprise features.
Generate high-resolution 1024x1024 images using Stable Diffusion XL Base on Tenstorrent hardware.
Create videos by generating frames with Stable Diffusion 3.5 - see hardware scaling from n150 to Galaxy!
Explore TT-Forge - Tenstorrent's MLIR-based compiler! Start with validated models like MobileNetV2.
Master TT-XLA - production-ready XLA compiler with simple wheel installation and multi-chip support.
Build an AI coding assistant using Llama 3.1 8B and prompt engineering.
Learn to build standalone packages outside TT-Metalium! Create animated videos with AnimateDiff temporal attention.
Learn the fundamentals of custom training on Tenstorrent hardware. Understand fine-tuning vs training from scratch.
Master dataset creation and validation for fine-tuning. Learn JSONL format, quality guidelines, and tokenization concepts.
Learn YAML-driven training configuration using tt-blacksmith patterns. Master hyperparameters and device configuration.
Train a character-level language model from scratch on Tenstorrent hardware. Watch NanoGPT learn Shakespeare!
Scale training to multiple Tenstorrent chips with Data Parallel (DDP). Achieve 2-8x speedup on n300, T3000, and Galaxy.
Master experiment tracking with file-based logging and Weights & Biases (WandB) integration. Make data-driven decisions!
Understand transformer architecture components before training from scratch. Learn embeddings, attention, and feed-forward networks.
Build and train a transformer from random initialization. Design nano-trickster (11M params) and understand scaling laws!
Welcome to the Tenstorrent Cookbook! Build 5 complete projects that teach fundamental TT-Metalium techniques.
Build Conway's Game of Life using TT-NN™ parallel tile computing. Learn convolution operations and cellular automata.
Build a real-time audio processing pipeline with TT-NN. Compute mel-spectrograms and apply creative effects.
Render beautiful fractals with interactive zoom! Demonstrates GPU-style parallel computation.
Build a library of creative image filters using 2D convolution. From edge detection to artistic effects!
Simulate emergent complexity from simple particle interactions! Features N² force calculations and multi-device acceleration.
Von Neumann architecture, fetch-decode-execute cycle, and RISC-V fundamentals.
Cache locality, bandwidth tradeoffs, and near-memory compute.
Amdahl's Law, SPMD patterns, and data parallelism. Scale from 1 to 880 cores!
Message passing, network topologies, and routing. Master the Network-on-Chip!
Race conditions, barriers, and coordination. Learn explicit synchronization!
From Python to machine code. Understand the compilation pipeline!
Big-O meets real hardware. See why constants matter in algorithm-hardware co-design!
The fastest way to get started! One-command installation of the full Tenstorrent stack.
Learn how to contribute by bringing up new models. Master TT-Metalium while becoming part of the ecosystem!
Discover what's possible! Explore TT-NN tutorials, browse the model zoo, and dive into programming examples.
💡 Note: These lessons assume you have a working Tenstorrent environment. If you need to set up from scratch, see the "Setup Information" section above or start with the TT-Installer lesson.
This extension creates scripts and files in a dedicated workspace directory:
~/tt-scratchpad/
All generated Python scripts, configuration files, and test code will be saved here. This keeps your workspace organized and makes it easy to find and customize the code.