TT-NN is a Python & C++ Neural Network OP library.
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.
| 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 | v0.62.0-dev20251015 | 1d799da |
| 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.
For the latest model updates and features, please see MODEL_UPDATES.md
For information on initial model procedures, please see Model Bring-Up and Testing
- 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)
- Matrix Multiply FLOPS on Wormhole and Blackhole (updated June 17th, 2025)
TT-Metalium is our low-level programming model, enabling kernel development for Tenstorrent hardware.
Get started with simple kernels.
- 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)
- Blackhole Bring-Up Programming Guide (Updated Dec 18th, 2024)
- Sub-Devices (Updated Jan 7th, 2025)
- Matmul OP on a Single_core
- Matmul OP on Multi_core (Basic)
- Matmul Multi_core Reuse (Optimized)
- Matmul Multi_core Multi-Cast (Optimized)
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-visualizerThe TT-Exalens repository describes TT-Lensium, a low-level debugging tool for Tenstorrent hardware. It allows developers to access and communicate with Wormhole and Blackhole devices.
The TT-SMI repository describes the Tenstorrent System Management Interface. This command line utility can interact with Tenstorrent devices on host. TT-SMI provides an easy to use interface displaying device, telemetry, and firmware information.
The Model Explorer is an intuitive and hierarchical visualization tool using model graphs. It organizes model operations into nested layers and provides features for model exploration and debugging.
The Tracy Profiler is a real-time nanosecond resolution, remote telemetry, hybrid frame, and sampling tool. Tracy supports profiling CPU, GPU, memory allocation, locks, context switches, and more.
DPRINT can print variables, addresses, and circular buffer data from kernels to the host terminal or log file. This feature is useful for debugging issues with kernels.
Watcher monitors firmware and kernels for common programming errors, and overall device status. If an error or hang occurs, Watcher displays log data of that occurrence.
Inspector provides insights into host runtime. It logs necessary data for investigation and allows queries to host runtime data.
| Release | Release Date |
|---|---|
| 0.64.0 | ETA Oct 15, 2025 |
| 0.63.0 | Sep 22, 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.
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!
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.