Overview
Tenstorrent’s software stack takes models from popular frameworks all the way down to Tensix cores. Most users start at the top with the TT-Forge compiler; the lower layers are available when you need finer control.
TT-Forge™
End-to-end MLIR compiler. Compile models from PyTorch, JAX, and ONNX — the recommended entry point.
TT-NN™
High-level Python API of pre-optimized neural-network operations.
TT-Lang™
Python DSL for authoring fused custom operations.
TT-MLIR™
MLIR-based backend compiler infrastructure that lowers models onto Tenstorrent hardware.
TT-Metalium™
Low-level C++ SDK for writing custom kernels on Tensix cores.
TT-NN™
High-level Python API of pre-optimized neural-network operations.
TT-Lang™
Python DSL for authoring fused custom operations.
TT-MLIR™
MLIR-based backend compiler infrastructure that lowers models onto Tenstorrent hardware.
TT-Metalium™
Low-level C++ SDK for writing custom kernels on Tensix cores.
Choosing your entry point
Compile a model from PyTorch, JAX, or ONNX → TT-Forge (recommended start).
Build networks in Python from pre-optimized ops → TT-NN.
Author custom fused operations → TT-Lang.
Write kernels directly on Tensix cores → TT-Metalium.
Work on compiler internals or custom backends → TT-MLIR.