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).

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.

2024年10月11日 · Description TensorRT int8 slower than FP16, Environment TensorRT Version: 10.2.0.19 GPU Type: RTX 3090 Nvidia Driver Version: 530.30.02 CUDA Version: 11.3 CUDNN Version: 8.2 Operating System + Version: Ubuntu 20.04.2 LTS Python Version (if applicable): 3.8.5 TensorFlow Version (if applicable): PyTorch Version (if applicable): 1.10 Baremetal or …

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

2023年4月20日 · Hello, I noticed in CUDA 12.1 update 1 that FP8 matrix multiples are now supported on Ada chips when using cuBLASLt. However, when I tried a benchmark on an RTX 4090 I was only able to achieve 1/2 of the rated throughput, around ~330-340 TFLOPS. My benchmark was a straightforward modification of the cuBLASLt FP8 sample to use larger …

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 …

2023年11月15日 · TOPs indicate INT8 performance. TFLOPs is used for the FP32 performance score. For example, in NVIDIA Jetson AGX Orin Series Technical Brief: Jetson AGX Orin 64GB … up to 170 Sparse TOPs of INT8 Tensor compute, and up to 5.3 FP32 TFLOPs of CUDA compute. Thanks.

2020年4月28日 · Hi experts, I have one Cascade Lake sever and run AI inference(INT8 precision) tasks with intel-tensorflow. According to Introduction to Intel® Deep Learning Boost on Second Generation Intel® Xeon® Scalable Processors, VNNI can speed up computing very much. And I do saw the time cost decreasing agai...

2017年9月7日 · Hello everyone, I am a newbee with TensorRT. I am trying to use TensorRT on my dev computer equipped with a GTX 1060. When optimizing my caffe net with my c++ program (designed from the samples provided with the library), I get the following message “Half2 support requested on hardware without native FP16 support, performance will be negatively affected.” …

2021年1月12日 · Hi, is there any reference for the peak performance of INT1, INT4, INT8, INT16, INT32 for RTX3090 on Tensorcore? Thanks!