deep-learning-benchmark/README.md at master · u39kun/deep-learning-benchmark · GitHub
Revisiting Volta: How to Accelerate Deep Learning - The NVIDIA Titan V Deep Learning Deep Dive: It's All About The Tensor Cores
Speed up TensorFlow Inference on GPUs with TensorRT — The TensorFlow Blog
NVIDIA A100 Deep Learning Benchmarks for TensorFlow | Exxact Blog
Just want to share some benchmarks I've done with the Zotac GeForce RTX 3070 Twin Edge OC, Tensorflow 1.x and Resnet-50. It looks that FP16 is not working as expected. Also is
Leveraging TensorFlow-TensorRT integration for Low latency Inference — The TensorFlow Blog
NVLINK on RTX 2080 TensorFlow and Peer-to-Peer Performance with Linux | Puget Systems
deep-learning-benchmark/README.md at master · u39kun/deep-learning-benchmark · GitHub
TITAN RTX Benchmarks for Deep Learning in TensorFlow 2019: XLA, FP16, FP32, & NVLink | Exxact Blog
Video Series: Mixed-Precision Training Techniques Using Tensor Cores for Deep Learning | NVIDIA Technical Blog
NVIDIA RTX 2080 Ti Benchmarks for Deep Learning with TensorFlow: Updated with XLA & FP16 | Exxact Blog
Educational Video] PyTorch, TensorFlow, Keras, ONNX, TensorRT, OpenVINO, AI Model File Conversion - YouTube
NVIDIA RTX 2080 Ti Benchmarks for Deep Learning with TensorFlow: Updated with XLA & FP16 | Exxact Blog
Post-Training Quantization of TensorFlow model to FP16 | by zong fan | Medium
Google Developers Blog: Announcing TensorRT integration with TensorFlow 1.7
Titan RTX Deep Learning Benchmarks
FP64, FP32, FP16, BFLOAT16, TF32, and other members of the ZOO | by Grigory Sapunov | Medium
Benchmarking GPUs for Mixed Precision Training with Deep Learning
NVIDIA TITAN RTX Deep Learning Benchmarks 2019 – Performance improvements with XLA, AMP and NVLink in TensorFlow | BIZON Custom Workstation Computers, Servers. Best Workstation PCs and GPU servers for AI/ML, deep
TensorFlow Model Optimization Toolkit — float16 quantization halves model size — The TensorFlow Blog
TensorFlow Model Optimization Toolkit — float16 quantization halves model size — The TensorFlow Blog