TensorRT Supported Model List

This verified model matrix pairs with import_workflows.md. For each model family, it lists the dtype(s) used during validation.

Scope & Reading Guide

TensorRT is a general-purpose neural-network graph execution engine, not a model zoo. In principle any NN architecture can run on TensorRT as long as it is expressible through the workflows described in the Import Workflows Guide. The Custom Plugin section covers the escape hatch for ops TensorRT does not yet implement natively.

The table below is not an exhaustive support list. It is the subset of models NVIDIA has verified and benchmarked; we publish it so you know which configurations have a known-good baseline and where the current rough edges are. If your model is not listed, the expectation is still that it works — please file an issue if it does not.

Reading the Tables

• Dtype lists the precision used for the verified baseline. Other precisions may also work.
• Component-split models (diffusion pipelines, speech models with encoder/decoder) list one row per validated component.

Table of Contents

• LLMs / Text Generation
• Encoder-only NLP (BERT family, embeddings)
• Vision Classification & Embeddings
• Speech / Audio
• Diffusion Models
• Multimodal
• Legacy / TRT Sample Models
• Requesting New Model Coverage

---

LLMs / Text Generation

Preferred path for LLM generation: TensorRT-LLM (KV-cache, paged attention, FP8/INT4, speculative decoding, tensor/pipeline parallelism). For production LLM serving, use TensorRT-LLM.

Model	Dtype
meta-llama/Llama-3.1-8B	bfloat16
meta-llama/Llama-3.2-1B	bfloat16
Qwen/Qwen3-0.6B	bfloat16
deepseek-ai/Janus-Pro-7B	bfloat16

For TensorRT-LLM's own coverage, see the TensorRT-LLM model support matrix.

---

Encoder-only NLP (BERT family, embeddings)

Model	Dtype
google-bert/bert-base-uncased	float32
google-bert/bert-base-multilingual-cased	float16
FacebookAI/roberta-base	float32
FacebookAI/roberta-large	float32
FacebookAI/xlm-roberta-base	float32
distilbert/distilbert-base-uncased	float32
sentence-transformers/all-MiniLM-L6-v2	float32
sentence-transformers/all-mpnet-base-v2	float32
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2	float32
BAAI/bge-base-en-v1.5	float32
nlpaueb/legal-bert-base-uncased	float32

---

Vision Classification & Embeddings

Model	Dtype
torchvision/resnet50	float32
timm/mobilenetv3_small_100.lamb_in1k	float32
trpakov/vit-face-expression	float32
openai/clip-vit-base-patch32	float32
openai/clip-vit-large-patch14	float32
facebook/dinov2-base	float32
Falconsai/nsfw_image_detection	float32
dima806/fairface_age_image_detection	float32

---

Speech / Audio

Model (Component)	Dtype
openai/whisper-large-v3-turbo (Encoder)	float32
openai/whisper-large-v3-turbo (Decoder)	float32
openai/whisper-large-v3 (Encoder)	float32
openai/whisper-large-v3 (Decoder)	float32
laion/clap-htsat-fused	float32
sesame/csm-1b (Backbone)	float32
neuphonic/neutts-air	float32
LiquidAI/LFM2-Audio-1.5B	float32

---

Diffusion Models

Diffusion pipelines are evaluated per component (Text Encoder / UNet or DiT / VAE) because TRT does not ingest the pipeline object directly.

Pipeline (Component)	Dtype
stabilityai/sd-turbo	float16
stabilityai/sdxl-turbo (UNet)	float16
stabilityai/sdxl-turbo (VAE / Text Encoders)	mixed
stabilityai/stable-diffusion-xl-base-1.0	float16
CompVis/stable-diffusion-v1-4	float16
stable-diffusion-v1-5/stable-diffusion-v1-5	float16
stabilityai/stable-diffusion-2-1	float16
playgroundai/playground-v2.5-1024px-aesthetic	float16
dataautogpt3/ProteusV0.3	float16
black-forest-labs/FLUX.2-dev (Text Encoder)	bfloat16
black-forest-labs/FLUX.2-dev (DiT)	bfloat16
black-forest-labs/FLUX.2-dev (VAE)	float16
black-forest-labs/FLUX.1-schnell (DiT / TextEnc / VAE)	mixed
Wan-AI/Wan2.2-T2V-A14B-Diffusers (Text Encoder)	float16
Wan-AI/Wan2.2-T2V-A14B-Diffusers (VAE)	float16
Qwen/Qwen-Image (Text Encoder)	bfloat16
Qwen/Qwen-Image (DiT / VAE)	bfloat16
stabilityai/stable-diffusion-3-medium-diffusers	bfloat16
stabilityai/stable-diffusion-3.5-medium / 3.5-large	mixed
HiDream-ai/HiDream-I1-Full	bfloat16
stabilityai/stable-video-diffusion-img2vid-xt	float16

---

Multimodal

Model	Dtype
openai/clip-vit-base-patch32	float32
deepseek-ai/Janus-Pro-7B	bfloat16
Datadog/Toto-Open-Base-1.0	float32

---

Legacy / TRT Sample Models

TensorRT ships hand-validated C++/Python samples for these classic architectures and workflows:

• MNIST digit classifiers, model parsing, dynamic-shape, plugin, and safe-runtime samples — see samples/ in this repo.

---

Requesting New Model Coverage

File a GitHub issue with:

1. The Hugging Face ID or model source URL.
2. The target dtype (fp32 / fp16 / bf16 / fp8 / int8 / int4).
3. Any framework-level working example (helps us reproduce quickly).

The maintainers will benchmark the model and extend this table — no external contributor action needed for the benchmark step.