TensorRT-LLMs/examples/pytorch

TRT-LLM with PyTorch

Run the quick start script:

python3 quickstart.py

Run the advanced usage example script:

# BF16
python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct

# FP8
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8

# BF16 + TP=2
python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct --tp_size 2

# FP8 + TP=2
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --tp_size 2

# FP8(e4m3) kvcache
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --kv_cache_dtype fp8

# BF16 + TP=8
python3 quickstart_advanced.py --model_dir nvidia/Llama-3_1-Nemotron-Ultra-253B-v1 --tp_size 8

# Nemotron-H requires disabling cache reuse in kv cache
python3 quickstart_advanced.py --model_dir nvidia/Nemotron-H-8B-Base-8K --disable_kv_cache_reuse --max_batch_size 8

Run the multimodal example script:

# default inputs
python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --modality image [--use_cuda_graph]

# user inputs
# supported modes:
# (1) N prompt, N media (N requests are in-flight batched)
# (2) 1 prompt, N media
# Note: media should be either image or video. Mixing image and video is not supported.
python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --modality video --prompt "Tell me what you see in the video briefly." "Describe the scene in the video briefly." --media "https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/OAI-sora-tokyo-walk.mp4" "https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/world.mp4" --max_tokens 128 [--use_cuda_graph]

Supported Models

Architecture	Model	HuggingFace Example	Modality
BertForSequenceClassification	BERT-based	textattack/bert-base-uncased-yelp-polarity	L
DeepseekV3ForCausalLM	DeepSeek-V3	deepseek-ai/DeepSeek-V3	L
Gemma3ForCausalLM	Gemma3	google/gemma-3-1b-it	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 Llama 3.1 Llama 2 LLaMA	meta-llama/Meta-Llama-3.1-70B	L
Llama4ForConditionalGeneration	Llama 4 Scout Llama 4 Maverick	meta-llama/Llama-4-Scout-17B-16E-Instruct meta-llama/Llama-4-Maverick-17B-128E-Instruct	L + V
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
NemotronHForCausalLM	Nemotron-H	nvidia/Nemotron-H-8B-Base-8K nvidia/Nemotron-H-47B-Base-8K nvidia/Nemotron-H-56B-Base-8K	L
NemotronNASForCausalLM	LLamaNemotron LlamaNemotron Super LlamaNemotron Ultra	nvidia/Llama-3_1-Nemotron-51B-Instruct nvidia/Llama-3_3-Nemotron-Super-49B-v1 nvidia/Llama-3_1-Nemotron-Ultra-253B-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.

Run the speculative decoding script:

# NGram drafter
python3 examples/pytorch/quickstart_advanced.py \
    --max_matching_ngram_size=2 \
    --spec_decode_nextn=4