Developers
Learn how to get your models up and running fast on Tenstorrent hardware.
With two open source SDKs, you can get as close to the metal as possible, or let our AI compiler do the work.
Model Support Table
Qwen 3 32B
QuietBox (Wormhole)
LLM
TP=8
QwQ 32B
QuietBox (Wormhole)
LLM
TP=8
DeepSeek R1 Distill Llama 3.3 70B
QuietBox (Wormhole)
LLM
TP=8
Llama 3.1 70B
Galaxy (Wormhole)
LLM
TP=32
Llama 3.1 70B
QuietBox (Wormhole)
LLM
TP=8
Llama 3.1 70B
QuietBox (Blackhole)
LLM
TP=4
Llama 3.2 11B Vision
n300 (Wormhole)
LLM
TP=2
Qwen 2.5 7B
n300 (Wormhole)
LLM
TP=2
Qwen 2.5 72B
QuietBox (Wormhole)
LLM
TP=8
Falcon 7B
n150 (Wormhole)
LLM
Falcon 7B
QuietBox (Wormhole)
LLM
DP=8
Falcon 7B
Galaxy (Wormhole)
LLM
DP=32
Falcon 40B
QuietBox (Wormhole)
LLM
TP=8
Llama 3.1 8B
p100 (Blackhole)
LLM
Llama 3.1 8B
p150 (Blackhole)
LLM
Llama 3.1 8B
2 x p150 (Blackhole)
LLM
DP=2
Llama 3.1 8B
n150 (Wormhole)
LLM
Llama 3.2 1B
n150 (Wormhole)
LLM
Llama 3.2 3B
n150 (Wormhole)
LLM
Mamba 2.8B
n150 (Wormhole)
LLM
Mistral 7B
n150 (Wormhole)
LLM
Mixtral 8x7B
QuietBox (Wormhole)
LLM
TP=8
Whisper (distil-large-v3)
n150 (Wormhole)
Speech-to-Text
Stable Diffusion 1.4
n150 (Wormhole)
Diffusion Model
512 x 512
Stable Diffusion 3.5 Medium
n150 (Wormhole)
Diffusion Model
512 x 512
ResNet-50
n150 (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
ResNet-50
n300 (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
DP=2
ResNet-50
QuietBox (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
DP=8
ResNet-50
Galaxy (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
DP=32
ViT-base
n150 (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
ViT-base
n300 (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
DP=2
ViT-base
QuietBox (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
DP=8
MobileNet-v2
n150 (Wormhole)
CNNs and Vision Transformer
(Classification model)
224 x 224
YOLOv4
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
320 x 320
YOLOv4
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
YOLOv8x
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
YOLOv8s
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
YOLOv8s_world
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
YOLOv9c
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
YOLOv10x
n150 (Wormhole)
CNNs and Vision Transformer
(Object Detection)
640 x 640
UNet - VGG19
n150 (Wormhole)
CNNs and Vision Transformer
(Segmentation)
256 x 256
SegFormer Semantic Segmentation
n150 (Wormhole)
CNNs and Vision Transformer
(Segmentation)
512 x 512
YOLOv9c
n150 (Wormhole)
CNNs and Vision Transformer
(Segmentation)
640 x 640
UFLD - v2
n150 (Wormhole)
CNNs and Vision Transformer
(Segmentation)
320 x 800
BERT-Large
n150 (Wormhole)
NLP
(Segmentation)
Sentence-Bert (backbone: bert-base)
n150 (Wormhole)
NLP
(Segmentation)
Getting started on Tenstorrent
TT-Forge™
TT-Forge™ is Tenstorrent's MLIR-based compiler.
TT-NN™
TT-NN™ is a user-friendly API for running ML workloads on Tenstorrent hardware.
TT-Metalium™
TT-Metalium™ is Tenstorrent’s open source, low level AI hardware SDK.
Looking for other documentation?
Active Bounties
Solve bugs and add features to win cash prizes, and get our open source software to stable releases even faster.
Upcoming Events
Aug 9
COSCUP 2025
Stop by our booth at the Conference for Open Source Coders, Users & Promoters (COSCUP), the largest open source conference in Asia.
Aug 13
Office Hours: TT-Forge
Come learn more about tt-forge, Tenstorrent's AI compiler as we gear up for our public beta. Find out how to access Tenstorrent hardware and share feedback.
Educational Content
Tutorials
Tutorials
A guide to the Metalium programming model, Tenstorrent’s primary programming model.
Tutorials
Learn how to install and run ttnn-visualizer. ttnn-visualizer helps you gain a complete understanding of a model.
Written Tutorials
Bring up LLMs with TTNN
Get guidance on how to bring up high-performance multi-chip models on Tenstorrent hardware using the TT-Metalium stack.
Op Writer's Guide to Dispatch Overhead
This tutorial covers different methods to optimize dispatch overhead resource allocation, kernel initialization, and runtime arguments.
Join the Community
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Interested in contributing?
Tenstorrent's AI software stack is open source. Getting started is as easy as filing an issue.