---
myst:
  html_meta:
    product-name: TT-Metalium™, TT-NN™, TT-Forge™, Tensix core
    technology-concepts: Machine Learning, High-Performance Computing, Neural Networks, Compilers, Multi-Level Intermediate Representation (MLIR), RISC-V, Network-on-Chip (NoC), Kernels, Jax, TensorFlow, PyTorch
    document-type: Explanation
---

# Understanding the Tenstorrent Software Stack
This document provides developers and researchers with an overview of the main components of the Tenstorrent software stack: TT-Metalium™, TT-NN™, and TT-Forge™. You will learn about each tool's purpose, use cases, and where to find its source code.

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## **TT-Metalium™: Programming Tenstorrent Hardware**
TT-Metalium™ is the low-level, open-source software development kit (SDK) that provides developers direct access to Tenstorrent hardware. It is a bare-metal programming environment designed for users who must write custom C++ kernels for machine learning or other high-performance computing workloads.

Use TT-Metalium™ when you require complete control over the hardware to optimize code for performance, explicitly manage memory, or implement novel operations not found in standard libraries. This environment exposes the RISC-V processors, the Network-on-Chip (NoC), and the matrix and vector engines within each Tensix core.

The main project is available in the [tt-metal](https://github.com/tenstorrent/tt-metal) GitHub repository.

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## **TT-NN™: A Python & C++ Neural Network OP library**

TT-NN™ is a library of neural network operations that provides a user-friendly interface for running models on Tenstorrent hardware. It is designed to be intuitive for developers familiar with [PyTorch](https://pytorch.org).

Use TT-NN™ to run AI models using a familiar, high-level Python API without managing the complexities of the underlying hardware. TT-NN™ builds upon TT-Metalium™ and provides a stable set of pre-packaged, optimized operations. It is also available with a C++ API.

The TT-NN™ library is part of the [tt-metal](https://github.com/tenstorrent/tt-metal) GitHub repository.

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## **TT-Forge™: The MLIR-Based Compiler**

TT-Forge™ is Tenstorrent’s Multi-Level Intermediate Representation (MLIR)-based compiler. It bridges high-level machine learning frameworks with the Tenstorrent software stack.

Use TT-Forge™ to compile models from frameworks such as [PyTorch](https://pytorch.org), [JAX](https://docs.jax.dev/en/latest/), and [TensorFlow](https://www.tensorflow.org) for execution on Tenstorrent hardware. It offers an automated, general path to run many types of model architectures without requiring custom kernel development. TT-Forge™ integrates with and lowers to TT-Metalium for hardware execution.

The main project is located in the [tt-forge](https://github.com/tenstorrent/tt-forge) GitHub repository.

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## **Exploring our Developer Hub**

To continue learning, visit and explore our [Developer Hub](https://tenstorrent.com/developers). There you will find in-depth articles, and information on our software bounty program. You can also find tutorials and technical overviews on our [YouTube channel](https://www.youtube.com/@tenstorrentinc).

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## **Engaging with our Community**
For real-time discussions, community Q\&A, and to share your latest projects, we invite you to join our [Discord channel](https://discord.gg/tenstorrent). It's a great place to engage directly with other developers and members of the Tenstorrent community.

As always, if you have any questions, please [raise a support request](https://tenstorrent.atlassian.net/servicedesk/customer/portal/1) with our team. We’re here to help, and are excited to see what you build.