TensorRT-LLMs/docs/source/reference/support-matrix.md

(support-matrix)=

Support Matrix

TensorRT-LLM optimizes the performance of a range of well-known models on NVIDIA GPUs. The following sections provide a list of supported GPU architectures as well as important features implemented in TensorRT-LLM.

Models (PyTorch Backend)

Architecture	Model	HuggingFace Example	Modality
BertForSequenceClassification	BERT-based	textattack/bert-base-uncased-yelp-polarity	L
DeciLMForCausalLM	Nemotron	nvidia/Llama-3_1-Nemotron-51B-Instruct	L
DeepseekV3ForCausalLM	DeepSeek-V3	deepseek-ai/DeepSeek-V3	L
LlavaLlamaModel	VILA	Efficient-Large-Model/NVILA-8B	L + V
LlavaNextForConditionalGeneration	LLaVA-NeXT	llava-hf/llava-v1.6-mistral-7b-hf	L + V
LlamaForCausalLM	Llama 3.1, Llama 3, Llama 2, LLaMA	meta-llama/Meta-Llama-3.1-70B	L
Llama4ForConditionalGeneration	Llama 4	meta-llama/Llama-4-Scout-17B-16E-Instruct	L
MistralForCausalLM	Mistral	mistralai/Mistral-7B-v0.1	L
MixtralForCausalLM	Mixtral	mistralai/Mixtral-8x7B-v0.1	L
MllamaForConditionalGeneration	Llama 3.2	meta-llama/Llama-3.2-11B-Vision	L
NemotronForCausalLM	Nemotron-3, Nemotron-4, Minitron	nvidia/Minitron-8B-Base	L
NemotronNASForCausalLM	NemotronNAS	nvidia/Llama-3_3-Nemotron-Super-49B-v1	L
Qwen2ForCausalLM	QwQ, Qwen2	Qwen/Qwen2-7B-Instruct	L
Qwen2ForProcessRewardModel	Qwen2-based	Qwen/Qwen2.5-Math-PRM-7B	L
Qwen2ForRewardModel	Qwen2-based	Qwen/Qwen2.5-Math-RM-72B	L
Qwen2VLForConditionalGeneration	Qwen2-VL	Qwen/Qwen2-VL-7B-Instruct	L + V
Qwen2_5_VLForConditionalGeneration	Qwen2.5-VL	Qwen/Qwen2.5-VL-7B-Instruct	L + V

Note:

• L: Language only
• L + V: Language and Vision multimodal support
• Llama 3.2 accepts vision input, but our support currently limited to text only.

Models (TensorRT Backend)

LLM Models

• Arctic
• Baichuan/Baichuan2
• BART
• BERT
• BLOOM
• ByT5
• ChatGLM
• ChatGLM2
• ChatGLM3
• Code LLaMA
• DBRX
• Exaone
• FairSeq NMT
• Falcon
• Flan-T5 ¹
• Gemma/Gemma2
• GLM-4
• GPT
• GPT-J
• GPT-Nemo
• GPT-NeoX
• Granite-3.0
• Grok-1
• InternLM
• InternLM2
• LLaMA/LLaMA 2/LLaMA 3/LLaMA 3.1
• Mamba
• mBART
• Minitron
• Mistral
• Mistral NeMo
• Mixtral
• MPT
• Nemotron
• mT5
• OPT
• Phi-1.5/Phi-2/Phi-3
• Qwen/Qwen1.5/Qwen2
• Qwen-VL
• RecurrentGemma
• Replit Code ²
• RoBERTa
• SantaCoder
• Skywork
• Smaug
• StarCoder
• T5
• Whisper

Multi-Modal Models ³

• BLIP2 w/ OPT
• BLIP2 w/ T5
• CogVLM ⁴
• Deplot
• Fuyu
• Kosmos
• LLaVA-v1.5
• LLaVa-Next
• LLaVa-OneVision
• NeVA
• Nougat
• Phi-3-vision
• Video NeVA
• VILA
• MLLaMA
• LLama 3.2 VLM

(support-matrix-hardware)=

Hardware

The following table shows the supported hardware for TensorRT-LLM.

If a GPU architecture is not listed, the TensorRT-LLM team does not develop or test the software on the architecture and support is limited to community support. In addition, older architectures can have limitations for newer software releases.

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* -
  - Hardware Compatibility
* - Operating System
  - TensorRT-LLM requires Linux x86_64 or Linux aarch64.
* - GPU Model Architectures
  -
    - [NVIDIA Blackwell Architecture](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/)
    - [NVIDIA Grace Hopper Superchip](https://www.nvidia.com/en-us/data-center/grace-hopper-superchip/)
    - [NVIDIA Hopper Architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/)
    - [NVIDIA Ada Lovelace Architecture](https://www.nvidia.com/en-us/technologies/ada-architecture/)
    - [NVIDIA Ampere Architecture](https://www.nvidia.com/en-us/data-center/ampere-architecture/)

(support-matrix-software)=

Software

The following table shows the supported software for TensorRT-LLM.

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* -
  - Software Compatibility
* - Container
  - [25.04](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)
* - TensorRT
  - [10.10](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)
* - Precision
  -
    - Hopper (SM90) - FP32, FP16, BF16, FP8, INT8, INT4
    - Ada Lovelace (SM89) - FP32, FP16, BF16, FP8, INT8, INT4
    - Ampere (SM80, SM86) - FP32, FP16, BF16, INT8, INT4[^smgte89]

Support for FP8 and quantized data types (INT8 or INT4) is not implemented for all the models. Refer to {ref}`precision` and [examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples) folder for additional information.

---

1. Encoder-Decoder provides general encoder-decoder functionality that supports many encoder-decoder models such as T5 family, BART family, Whisper family, NMT family, and so on. ↩︎

2. Replit Code is not supported with the transformers 4.45+. ↩︎

3. Multi-modal provides general multi-modal functionality that supports many multi-modal architectures such as BLIP2 family, LLaVA family, and so on. ↩︎

4. Only supports bfloat16 precision. ↩︎