ttnn 0.63.0

General compute framework for Tenstorrent devices

Hardware | Install | Discord | Join Us | Bounty $

TT-NN is a Python & C++ Neural Network OP library.

API Reference | Model Demos

Quick Links

• TT-Forge
• TT-Forge-FE
• TT-Torch
• TT-XLA
• TT-MLIR
• TT-TVM
• Releases

Featured Models

The Models team is focused on developing the following models to a customer-ready state. Ongoing work includes optimizations for performance, accuracy, and compatibility. Follow each model link for more details.

[!IMPORTANT] For a full model list see the Model Matrix, or visit the Developer Hub.

[!NOTE] Performance Metrics:

• Time to First Token (TTFT) measures the time (in milliseconds) it takes to generate the first output token after input is received.
• T/S/U (Tokens per Second per User): Represents the throughput of first-token generation after prefill. It is calculated as 1 / inter-token latency.
• T/S (Tokens per Second): Represents total token throughput, calculated as T/S = T/S/U x batch size.
• TP (Tensor Parallel) and DP (Data Parallel): Indicate the parallelization factors across multiple devices.
• Reported LLM Performance: Based on an input sequence length of 128 tokens for all models.
• Performance Data Source: Metrics were collected using the tt-metal model demos (linked above). Results may vary when using other runtimes such as the vLLM inference server.

Llama 3.1 70B (TP=32)

Batch	Hardware	TTFT (MS)	T/S/U	Target T/S/U	T/S	TT-Metalium Release	vLLM Tenstorrent Repo Release
32	Galaxy (Wormhole)	53	72.5	80	2268.8	v0.62.2	c348d08

Qwen 3 32B (TP=8)

Batch	Hardware	TTFT (MS)	T/S/U	Target T/S/U	T/S	TT-Metalium Release	vLLM Tenstorrent Repo Release
32	QuietBox (Wormhole)	109	22.1	30	707.2	v0.59.0-rc52	f028da1

Blackhole software optimization is under active development. Please join us in shaping the future of open source AI!
[Discord] [Developer Hub]

For more information regarding vLLM installation and environment creation visit the Tenstorrent vLLM repository.

Model Updates

For the latest model updates and features, please see MODEL_UPDATES.md

Model Bring-Up and Testing

For information on initial model procedures, please see Model Bring-Up and Testing

TT-NN Tech Reports

• Advanced Performance Optimizations for Models (updated March 4th, 2025)
• Programming Mesh of Devices (updated Sept 9th, 2024)
• ViT Implementation in TT-NN on GS (updated Sept 22nd, 2024)
• LLMs Bring up in TT-NN (updated Oct 29th, 2024)
• YOLOv4 Implementation in TT-NN on WH (updated November 8th, 2024)
• CNN Bring up & Optimization in TT-NN (updated Jan 22nd, 2025)

Benchmarks

• Matrix Multiply FLOPS on Wormhole and Blackhole (updated June 17th, 2025)

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TT-Metalium is our low-level programming model, enabling kernel development for Tenstorrent hardware.

Programming Guide | API Reference

Getting started

Get started with simple kernels.

TT-Metalium Tech Reports

• Matrix Engine (updated Sept 6th, 2024)
• Data Formats (updated Sept 7th, 2024)
• Reconfiguring Data Formats (updated Oct 17th, 2024)
• Handling special floating-point numbers (updated Oct 5th, 2024)
• Allocator (Updated Dec 19th, 2024)
• Tensor Layouts (updated Sept 6th, 2024)
• Saturating DRAM Bandwidth (updated Sept 6th, 2024)
• Flash Attention on Wormhole (updated Sept 6th, 2024)
• CNNs on TT Architectures (updated Sept 6th, 2024)
• Ethernet and Multichip Basics (Updated Sept 20th, 2024)
• Collective Communication Library (CCL) (Updated Sept 20th, 2024)
• Blackhole Bring-Up Programming Guide (Updated Dec 18th, 2024)
• Sub-Devices (Updated Jan 7th, 2025)

TT-Metalium Programming Examples

Hello World

• Hello World! Compute Kernel
• Hello World! Data Movement Kernel

Add Integers

• Add 2 Integers in Baby RiscV
• Add 2 Integers in Compute Kernel

Simple Tensor Manipulation

• Sharding
• Padding

DRAM Data Movement

• Dram Loopback Data Movement

Eltwise

• Eltwise Unary OP in Vector Engine (SFPU)
• Eltwise Binary OP in Matrix Engine (FPU)

Matmul

• Matmul OP on a Single_core
• Matmul OP on Multi_core (Basic)
• Matmul Multi_core Reuse (Optimized)
• Matmul Multi_core Multi-Cast (Optimized)

Tools and Instruments

TT_NN Visualizer

A comprehensive tool for visualizing and analyzing model execution, offering interactive graphs, memory plots, tensor details, buffer overviews, operation flow graphs, and multi-instance support with file or SSH-based report loading. Install via pip or build from source:

pip install ttnn-visualizer

Related Tenstorrent Projects

• TT-Forge
• TT-Forge-FE
• TT-Torch
• TT-XLA
• TT-MLIR
• TT-TVM

Latest Releases

Release	Release Date
0.63.0	ETA Sep 15, 2025
0.62.2	Aug 20, 2025
0.61.0	Skipped
0.60.1	Jul 22, 2025
0.59.0	Jun 18, 2025
0.58.0	May 13, 2025
0.57.0	Apr 15, 2025
0.56.0	Mar 7, 2025

Visit the releases folder for details on releases, release notes, and estimated release dates.

Tenstorrent Bounty Program Terms and Conditions

This repo is a part of Tenstorrent’s bounty program. If you are interested in helping to improve tt-metal, please make sure to read the Tenstorrent Bounty Program Terms and Conditions before heading to the issues tab. Look for the issues that are tagged with both “bounty” and difficulty level!

License

TT-Metalium and TTNN are licensed under the Apache 2.0 License, as detailed in LICENSE and LICENSE_understanding.txt.

Some distributable forms of this project—such as manylinux-compliant wheels—may need to bundle additional libraries beyond the standard Linux system libraries. For example:

• libnuma
• libhwloc
• openmpi (when built with multihost support)
• libevent (when built with multihost support)

These libraries are bound by their own license terms.