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

Hardware

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

If a GPU is not listed, it is important to note that TensorRT-LLM is expected to work on GPUs based on the Volta, Turing, Ampere, Hopper, and Ada Lovelace architectures. Certain limitations may, however, apply.

	Hardware Compatibility
Operating System	TensorRT-LLM requires Linux x86_64 or Windows.
GPU Model Architectures	NVIDIA Hopper Architecture NVIDIA Ada Lovelace Architecture NVIDIA Ampere Architecture NVIDIA Turing Architecture NVIDIA Volta Architecture (experimental)

Software

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

	Software Compatibility
Container	24.07
TensorRT	10.4
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[4] Turing (SM75) - FP32, FP16, INT8[5], INT4 Volta (SM70) - FP32, FP16, INT8[5], INT4[6]

Note

Support for FP8 and quantized data types (INT8 or INT4) is not implemented for all the models. Refer to Numerical Precision and examples folder for additional information.