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

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.

:header-rows: 1
:widths: 20 80

* -
  - 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.

:header-rows: 1
:widths: 20 80

* -
  - 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. ↩︎