개발자

Tenstorrent 하드웨어에서 모델을 빠르게 실행하세요. 두 개의 오픈 소스 SDK로 가능한 한 금속에 가까이 접근하거나 AI 컴파일러에 맡길 수 있습니다.

모델

Explore models optimized on Tenstorrent hardware.

Don’t see yours listed? Check out TT-Forge, our compiler, to get other models running today.

모델46
bge-large-en-v1.5

Purpose-built for semantic search, dense retrieval, and RAG. BAAI, 335M.

Feature Extraction
335M
BGE-M3

Multi-lingual, multi-granularity retrieval across dense, sparse, and multi-vector modes. BAAI, 568M.

Feature Extraction
568M
DeepSeek-R1

Mixture-of-experts reasoning powerhouse rivaling closed frontier models on math and code. DeepSeek, 671B.

Text Generation
671B
distil-large-v3

Distilled Whisper large-v3 — near-parity transcription at roughly six times the speed. Distil-Whisper, 756M.

Speech-to-Text
756M
Falcon-40B-Instruct

Commercially licensed generalist trained on 1T tokens of curated web data. TII, 40B.

Text Generation
40B
Falcon-7B-Instruct

Commercially licensed instruction model — lean resource footprint. TII, 7B.

Text Generation
7B
FLUX.1 [dev]

Flow-matching transformer for photorealistic text-to-image with strong prompt adherence. Black Forest Labs, 12B.

Text-to-Image
12B
FLUX.1 [schnell]

FLUX distilled to 4 steps — full quality, fraction of the compute. Black Forest Labs, 12B.

Text-to-Image
12B
gemma-3-27b

Largest Gemma 3 base — 140-language reasoning, coding, long context. Google, 27B.

Text Generation
27B
gemma-3-4b-it

Compact Gemma 3 base — punches above its weight in reasoning and code. Google, 4B.

Text Generation
4B
gemma-3-4b-it

Punches above its weight in reasoning and code. Google, 4B.

Text Generation
4B
gpt-oss-120b

Open GPT-style model for deep reasoning in self-hosted deployments. GPT-OSS, 120B.

Text Generation
120B
gpt-oss-20b

Open GPT-style generation at a practical self-hosted scale. GPT-OSS, 20B.

Text Generation
20B
Llama-3.1-8B

128K context and multilingual base with a wide fine-tuning ecosystem. Meta, 8B.

Text Generation
8B
Llama-3.1-8B-Instruct

Multilingual instruction following, tool use, and function calling. Meta, 8B.

Text Generation
8B
Llama-3.2-11B-Vision

Image + text inputs on Llama's reasoning foundation, 128K context. Meta, 11B.

Image-Text-to-Text
11B
Llama-3.2-1B-Instruct

Instruction-tuned to run anywhere — on-device at 1B. Meta, 1B.

Text Generation
1B
Llama-3.2-3B-Instruct

Lightweight agent backbone for low-latency, resource-constrained deployments. Meta, 3B.

Text Generation
3B
Llama-3.2-90B-Vision

Image + text inputs at full scale on Llama's reasoning foundation. Meta, 90B.

Image-Text-to-Text
90B
Llama-3.3-70B

Llama 3.1 refined — better math, code, and multilingual, same 128K context. Meta, 70B.

Text Generation
70B
Llama-3.3-70B-Instruct

Stronger structured tasks, tool use, and reasoning than prior Llama generations. Meta, 70B.

Text Generation
70B
Mistral-7B

Sliding window and grouped-query attention for fast, memory-lean inference. Mistral AI, 7B.

Text Generation
7B
Mistral-7B-Instruct-v0.3

Function-calling and instruction following for production and agentic use. Mistral AI, 7B.

Text Generation
7B
Mixtral-8x7B

Sparse MoE: 13B active per token out of 45B total — quality at low per-token cost. Mistral AI, 45B.

Text Generation
45B
Motif Vision 6B Preview

Preview text-to-video from natural language prompts. Motif, 6B.

Text-to-Video
6B
Phi-3-Mini-128K-Instruct

Compact 3.8B instruction model with 128K context tuned for reasoning on constrained hardware. Microsoft, 3.8B.

Text Generation
3.8B
Qwen2.5-32B

General-purpose 32B text model with strong multilingual and code coverage. Alibaba, 32B.

Text Generation
32B
Qwen2.5-7B-Instruct

Tuned for code, math, and structured output at an efficient scale. Alibaba, 7B.

Text Generation
7B
Qwen2.5-Coder-32B

Code-specialist 32B tuned for program synthesis, repair, and structured output. Alibaba, 32B.

Text Generation
32B
Qwen2.5-VL-32B

Vision-language base — documents, charts, and multi-image Q&A. Alibaba, 32B.

Image-Text-to-Text
32B
Qwen2.5-VL-32B-Instruct

Document understanding, chart analysis, and multi-image Q&A. Alibaba, 32B.

Image-Text-to-Text
32B
Qwen2.5-VL-3B-Instruct

Vision-language for low-latency multimodal deployment on constrained hardware. Alibaba, 3B.

Image-Text-to-Text
3B
Qwen2.5-VL-72B

Vision-language base at scale — documents, charts, and scene understanding. Alibaba, 72B.

Image-Text-to-Text
72B
Qwen2.5-VL-72B-Instruct

Vision-language at scale — documents, charts, and scene understanding. Alibaba, 72B.

Image-Text-to-Text
72B
Qwen3-32B

Toggleable chain-of-thought for on-demand deep reasoning. Alibaba, 32B.

Text Generation
32B
Qwen3.6-27B

Mid-scale Qwen3.6 for general reasoning, coding, and multilingual generation. Alibaba, 27B.

Text Generation
27B
Qwen3-Embedding-8B

Higher-capacity multilingual embeddings for retrieval and reranking. Alibaba, 8B.

Embedding
8B
Qwen3-VL-32B-Instruct

Hybrid thinking + vision — documents, charts, UI, and multi-image tasks. Alibaba, 32B.

Image-Text-to-Text
32B
QwQ-32B

Chain-of-thought with self-reflection for math, science, and logic. Alibaba, 32B.

Text Generation
32B
SpeechT5 (TTS task)

Mix-transformer segmentation without positional encoding — accurate at low compute. NVIDIA, 3.8M.

Text-to-Speech
307M
Stable Diffusion 3.5 Large

Unified encoder-decoder for natural text-to-speech synthesis. Microsoft, 307M.

Text-to-Image
8B
SD-XL 1.0-base

MMDiT architecture — strong text rendering and prompt control for image generation. Stability AI, 8B.

Text-to-Image
6.6B
Wan2.2 (I2V)

Image-to-video generation animating a still frame with coherent motion. Alibaba, 14B active.

Image-to-Video
14B
Wan2.2

Causal video transformer for text-to-video with strong motion coherence. Alibaba, 14B.

Text-to-Video
14B
whisper-large-v3

99 languages, 680K hours of training audio — built for robust speech recognition. OpenAI, 1.5B.

Speech-to-Text
1.5B
Z-Image-Turbo

Few-step distilled text-to-image generation. Tongyi-MAI.

Text-to-Image
6B

버그 바운티 프로그램

우리와 함께 열린 미래를 만들어 가세요. 버그를 수정하고, 기능을 추가하며, 보상을 받으세요.

모두 보기
Sep 16
SFPLOADMACRO produces wrong results under coverage instrumentation (Reciprocal fails 46/153 on Blackhole)
medium
Sep 4
Fix INT_MIN correctness in int32 div, remainder, fmod, and scalar promotion
hard
Sep 4
DRAM-sharded decode matmul: the in0 multicast is one hop per activation shard
hard
Sep 2
binary_ng/ternary: replicate the broadcast row with local NoC copies instead of ~2000 scalar stores per tile
medium
Aug 27
ttnn.prod_bw returns non-finite gradients for zero inputs
medium
Aug 15
ttnn.softplus takes the linear branch at exactly input*beta == threshold (strict < where reference uses <=)
medium
Aug 12
Perf - top-k/sort/moe/sampling index tile is written with 1024 scalar stores per tile on a DM core
hard
Aug 6
Generalize multi_scale_deformable_attn to support D values that are multiples of 16
medium
Aug 5
Fix Blackhole destination-reuse synchronization
hard
Aug 4
ttnn/normalization: sharded softmax accepts a subblock_w larger than Dest and silently returns wrong results
medium

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