2025年1月29日 · My ORIN AGX developer kit has the following specs: Jetpack 6.0 L4T 36.3.0 Cuda: 12.2 Pytorch: 2.3.0. While running some LLM Inference code locally using the Transformers library and using BitsandBytes to quantize the models to INT8 and INT4, I noticed that the GPU is not being utilized fully (it stays at 99% when performing inference in FP16).

2024年11月28日 · Hi, I’m looking for an explanation of how int8 TensorRT ops with multiple inputs are implemented, for example element-wise addition. In particular, I’m wondering how things work when the two inputs have very different quantization scales. One implementation I can image is just loading each of the int8 input tensors, de-quantizing each using its own quantization scale, …

2024年2月12日 · Hardware Platform (Jetson / GPU) Orin Nano DeepStream Version :6.3 JetPack Version (valid for Jetson only) 5.1.2-b104 TensorRT Version 8.5.2-1+cuda11.4 Issue Type Question I have a working yolo_v4_tiny model onnx file. Running deepstream converts it to fp16-engine, but this works on limits of 6 gb RAM of Jetson Orin Nano and slows/crashes. I would …

2022年9月20日 · After model INT8 quantization, we can reduce the computational resources and memory bandwidth required for model inference to help improve the model's overall performance. Unlike Quantization-aware Training (QAT) method, no re-train, or even fine-tuning is needed for POT optimization to obtain INT8 models with great accuracy.

2023年12月13日 · Hello, I just install jetpack 5.1.2 to my JNO 8GB. I installed ultralytics and resolved the Pytorch with Cuda. I started to benchmark yolov8 models from ultralytics package and I have same performance for fp32 and int8 configuration (fp16 is, as expected, half of fp32). Is this a problem with the int8 support in the jetson nano orin??? Thanks in advance. test.py …

2019年8月27日 · The idea behind INT8 is that the model may detect perfectly well even with this loss of accuracy. And yes, INT8 is supposed to improve performance. There is no reason to run an FP32 model if INT8 does the job, for INT8 will likely run faster. Keep in mind though that INT8 is still somewhat restrictive - not all layers can be converted to INT8.

2025年2月19日 · openVINO benchmark_app : this tool gives 2 option to specifiy precision --infer_precision Optional. Specifies the inference precision.

2021年5月11日 · Hi there, As stated here , I was able to calibrate and generate an int8 engine in the YOLO example. However, the performance(mAP) of the int8 model dropped about 7-15% compared with the fp32 model. Is this normal? How can I improve it? My setup is the following: Jetson Xavier DeepStream 5.0 JetPack 4.4 TensorRT 7.1.3 NVIDIA GPU Driver Version 10.2

2024年10月9日 · I am trying to convert onnx model to tensorrt egnine. I am using trtexec utility for doing this. Engine file should run in int8 so i generated a calibration file using qdqtranslator which converts qat model to ptq model. But when using the calibration file to convert to int8 , …

2021年4月1日 · There are two normal outputs of onnx-model: output_loc: 3cc41874 output_conf: 3c1047bf After doing the int8 calibration data set, there are two more outputs in the cache file: (Unnamed Layer* 315) [Shuffle]_output: 3d0743b7 (Unnamed Layer* 316) [Softmax]_output: 3c1047bf The calibration data set adopts 1000 test sets and the c...