ttnn 0.62.0rc29

General compute framework for Tenstorrent devices

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

API Reference | Model Demos

Latest Releases

Release	Release Date
0.62.0	ETA Aug 13, 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

LLMs

Model	Batch	Hardware	ttft (ms)	t/s/u	Target t/s/u	t/s	TT-Metalium Release	vLLM Tenstorrent Repo Release
Qwen 3 32B (TP=8)	32	QuietBox (Wormhole)	109	22.1	30	707.2	v0.59.0-rc52	f028da1
QwQ 32B (TP=8)	32	QuietBox (Wormhole)	133	25.2	30	806.4	v0.56.0-rc51	e2e0002
DeepSeek R1 Distill Llama 3.3 70B (TP=8)	32	QuietBox (Wormhole)	159	15.9	20	508.8	v0.59.0-rc53	f028da1
Llama 3.1 70B (TP=32)	32	Galaxy	68	66.7	80	2134.4	v0.60.0-rc20	5cbc982
Llama 3.1 70B (TP=8)	32	QuietBox (Wormhole)	159	15.9	20	508.8	v0.59.0-rc53	f028da1
Llama 3.1 70B (TP=4)	32	QuietBox (Blackhole)	195*	14.9*		476.5*	v0.59.0-rc53	f028da1
Llama 3.2 11B Vision (TP=2)	16	n300	2550	15.8	17	252.8	v0.56.0-rc6	e2e0002
Qwen 2.5 7B (TP=2)	32	n300	126	32.5	38	1040.0	v0.56.0-rc33	e2e0002
Qwen 2.5 72B (TP=8)	32	QuietBox (Wormhole)	319	14.6	20	467.2	v0.59.0-rc52	f028da1
Falcon 7B	32	n150	70	18.5	26	592.0	v0.60.0-rc20
Falcon 7B (DP=8)	256	QuietBox (Wormhole)	87	15.9	26	4070.4	v0.60.0-rc20
Falcon 7B (DP=32)	1024	Galaxy	121	13.2	26	13516.8	v0.60.0-rc20
Falcon 40B (TP=8)	32	QuietBox (Wormhole)		11.9	36	380.8	v0.59.0-rc38
Llama 3.1 8B	32	p100	87*	26.5*		848.0*	v0.59.0-rc3	739dcaa
Llama 3.1 8B	32	p150	69*	29.1*		931.2*	v0.59.0-rc3	739dcaa
Llama 3.1 8B (DP=2)	64	2 x p150	64*	18.6*		1190.4*	v0.59.0-rc3	739dcaa
Llama 3.1 8B	32	n150	104	24.8	23	793.6	v0.59.0-rc52	f028da1
Llama 3.2 1B	32	n150	23	72.6	160	2323.2	v0.59.0-rc52	f028da1
Llama 3.2 3B	32	n150	53	43.5	60	1392.0	v0.59.0-rc52	f028da1
Mamba 2.8B	32	n150	35	14.1	41	451.2	v0.59.0-rc38
Mistral 7B	32	n150	101	28.3	23	905.6	v0.59.0-rc52	f028da1
Mixtral 8x7B (TP=8)	32	QuietBox (Wormhole)	207	16.6	33	531.2	v0.59.0-rc53

Last Update: July 21, 2025

Notes:

• ttft = time to first token | t/s/u = tokens/second/user | t/s = tokens/second; where t/s = t/s/u * batch.
• TP = Tensor Parallel, DP = Data Parallel; Defines parallelization factors across multiple devices.
• The reported LLM performance is for an input sequence length (number of rows filled in the KV cache) of 128 for all models except Mamba (which can accept any sequence length).
• The t/s/u reported is the throughput of the first token generated after prefill, i.e. 1 / inter token latency.
• Performance numbers were collected using the tt-metal model demos (accessible via the model links). If running with a vLLM inference server, performance may be different.
• * 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.

Speech-to-Text

Model	Batch	Hardware	ttft (ms)	t/s/u	Target t/s/u	t/s	TT-Metalium Release
Whisper (distil-large-v3)	1	n150	232	58.1	45	58.1	v0.59.0-rc52

Diffusion Models

Model	Batch	Hardware	Sec/Image	Target Sec/Image	Release
Stable Diffusion 1.4 (512x512)	1	n150	6.25	3
Stable Diffusion 3.5 Medium (512x512)	1	n150	16	10

Notes:

• Stable Diffusion sec/image is based on the time elapsed from submitting the input prompt to receiving the image from the VAE decoder.

CNNs and Vision Transformers

Classification models

Model	Batch	Hardware	Image/sec	Target Image/sec	Release
ResNet-50 (224x224)	16	n150	4,700	7,000	v0.59.0
ResNet-50 (224x224) (DP=2)	32	n300	9,200	14,000	v0.59.0
ResNet-50 (224x224) (DP=8)	128	QuietBox (Wormhole)	35,800	56,000	v0.59.0
ResNet-50 (224x224) (DP=32)	512	Galaxy	96,800	224,000	v0.59.0
ViT-base (224x224)	8	n150	1,370	1,600	v0.60.0-rc4
ViT-base (224x224) (DP=2)	16	n300	1,900	3,200	v0.60.0-rc4
ViT-base (224x224) (DP=8)	64	QuietBox (Wormhole)	7,700	12,800	v0.60.0-rc4
MobileNet-v2 (224x224)	10	n150	2,808	3,500

Object Detection

Model	Batch	Hardware	Frame/sec (FPS)	Target FPS	Release
YOLOv4 (320x320)	1	n150	120	320
YOLOv4 (640x640)	1	n150	50	180
YOLOv8x (640x640)	1	n150	45	100
YOLOv8s (640x640)	1	n150	175	320
YOLOv8s_world (640x640)	1	n150	57	200
YOLOv9c (640x640)	1	n150	55	320
YOLOv10x (640x640)	1	n150	26	200

Segmentation

Model	Batch	Hardware	Frame/sec (FPS)	Target FPS	Release
UNet - VGG19 (256x256)	1	n150	77	150
SegFormer Semantic Segmentation (512x512)	1	n150	84	300
YOLOv9c (640x640)	1	n150	40	240
UFLD - v2 (320x800)	1	n150	255	2000

NLPs

Model	Batch	Hardware	Sentence/sec	Target sentence/sec	Release
BERT-Large	8	n150	270	400
Sentence-Bert (backbone: bert-base)	8	n150	403	550
Sentence-Bert (backbone: bert-base)	64	QuietBox	2961	4400

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

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.