GPT-J 6B inference on TensorRT with INT-8 precision

Repository contains inference example and accuracy validation of quantized GPT-J 6B TensorRT model. Prebuilt TensorRT engines are published on Hugging Face 🤗. Our example notebook automatically downloads the appropriate engine.

Currently published engines for the following GPUs only:

• RTX 2080 Ti
• RTX 3080 Ti
• RTX 4090

ONNX model and build script will be published later.

Metrics:

	TensorRT INT8+FP32	torch FP16	torch FP32
Lambada Acc	78.79%	79.17%	-
Model size (GB)	8.5	12.1	24.2

Test environment

• GPU RTX 4090
• CPU 11th Gen Intel(R) Core(TM) i7-11700K
• TensorRT 8.5.3.1
• pytorch 1.13.1+cu116

Latency:

Input sequance length	Number of generated tokens	TensorRT INT8+FP32 ms	torch FP16 ms	Acceleration
64	64	1040	1610	1.55
64	128	2089	3224	1.54
64	256	4236	6479	1.53
128	64	1060	1619	1.53
128	128	2120	3241	1.53
128	256	4296	6510	1.52
256	64	1109	1640	1.49
256	128	2204	3276	1.49
256	256	4443	6571	1.49

Test environment

• GPU RTX 4090
• CPU 11th Gen Intel(R) Core(TM) i7-11700K
• TensorRT 8.5.3.1
• pytorch 1.13.1+cu116