ttnn 0.59.0rc61

General compute framework for Tenstorrent devices

Install | Buy Hardware | Bounty $ | Join Us | Discord

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

API Reference | Model Demos

Latest Releases

Release	Release Date
0.60.0	Jul 18, 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	109	22.1	30	707.2	v0.59.0-rc52	f028da1
QwQ 32B (TP=8)	32	QuietBox	133	25.2	30	806.4	v0.56.0-rc51	e2e0002
DeepSeek R1 Distill Llama 3.3 70B (TP=8)	32	QuietBox	159	15.9	20	508.8	v0.59.0-rc53	f028da1
Llama 3.1 70B (TP=32)	32	Galaxy	105	59.1	80	1891.2	v0.59.0-rc52	f028da1
Llama 3.1 70B (TP=8)	32	QuietBox	159	15.9	20	508.8	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	319	14.6	20	467.2	v0.59.0-rc52	f028da1
Falcon 7B	32	n150	70	18.5	26	592.0	v0.59.0-rc52
Falcon 7B (DP=8)	256	QuietBox	87	15.9	26	4070.4	v0.59.0-rc38
Falcon 7B (DP=32)	1024	Galaxy	125	12.9	26	13209.6	v0.59.0-rc52
Falcon 40B (TP=8)	32	QuietBox		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	207	16.6	33	531.2	v0.59.0-rc53

Last Update: June 23, 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

CNNs

Model	Batch	Hardware	fps	Target fps	Release
ResNet-50 (224x224)	16	n150	4,700	7,000
ResNet-50 (224x224) (DP=2)	32	n300	9,200	14,000
ResNet-50 (224x224) (DP=8)	128	QuietBox	35,800	56,000
ResNet-50 (224x224) (DP=32)	512	Galaxy	96,800	224,000
ViT (224x224)	8	n150	1370	1,600
Stable Diffusion 1.4 (512x512)	1	n150	0.160	0.3
YOLOv4 (320x320)	1	n150	120	300
YOLOv4 (640x640)	1	n150	50	100
SegFormer Semantic Segmentation (512x512)	1	n150	90	300
Stable Diffusion 3.5 medium (512x512)	1	n150	0.06	0.3

Notes:

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

NLPs

Model	Batch	Hardware	sen/sec	Target sen/sec	Release
BERT-Large	8	n150	270	400

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 WH (updated November 13th, 2024)

---

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.