2024年11月20日 · Hello, I have an issue with one of two RTX Titan cards in a deep-learning workstation. If I run a deep-learning workload or a stress test on the machine, one card will top out at 78°C with a fan speed of about 60% , ~270W power consumption and GPU clocks around 1860 MHz, which seems to be OK. However, the other card under the same load will reach a steady …

2018年12月28日 · I primarily do numerical work with nvidia GPUs. Can someone give information on the relative performance of the Titan RTX and Titan V. What are the advantages and disadvantages for each. Are there specific applications where one should be preferred over the other? Based purely on price it would seem that the Titan V should be preferred for compute, …

2018年10月16日 · At reference specifications, peak theoretical tensor throughput is around 107.6 TFLOPS for the RTX 2080 Ti, 80.5 TFLOPS for the RTX 2080, and 59.7 TFLOPS for the RTX 2070. Unlike the 89% efficiency with the Titan V’s 97.5 TFLOPS, the RTX cards are essentially at half that level, with around 47%, 48%, and 45% efficiency for the RTX 2080 Ti ...

2019年2月1日 · on Titan RTX and also Turing-family GeForce GPUs such as 2080/2080Ti, P2P is only supported if/when the NVLink bridge is in place (i.e. only over NVLink). For Turing family GeForce GPUs without a NVLink bridge option, P2P is not supported. What NVIDIA supports is determined by the tool already run.

2018年12月19日 · Yes, TITAN RTX NVLINK BRIDGE only bridges 2 RTX GPUs together. NVLink won’t virtualize the GPUs. You’ll still see multiple GPUs. You’ll just have double the effective GPU memory capacity to 48 GB and scale performance with up …

2022年8月21日 · The NVIDIA TITAN RTX is the world’s most powerful desktop GPU, providing performance for AI research, data science, and creative applications. “Turing is NVIDIA’s biggest advance in a decade – fusing shaders, ray tracing, and deep learning…

2019年7月29日 · The problem Titan RTX seems to be locked at 300MHz at idle, and during full cuda load it might drop to 150MHz but never goes above 300MHz, even though gpu utilization says 100%. It pulls 100W at idle and doesn’t go up past 170W at full cuda load even though utilization says 100%

2019年7月17日 · The NVIDIA drivers that I have downloaded and installed from the page for Titan RTX are the Linux 64-bit Operating system and Linux 64-bit Ubuntu 18.04 Operating System. Thank You and Regards, YiYang

2021年4月8日 · I have a server with Two type GPU (RTX3090, TITAN RTX) Nvidia driver: 460.32.03 CUDA :11.1 Pytorch: 1.8.0 I’m trying to use those together for Deeplearning training using Pytorch. when i use one type GPU(RTX3090 or TITAN RTX) it works but when i use both GPU it does not work. i use nn.DataParallel for distribute weight optimize

2024年8月13日 · ValueError: Bfloat16 is only supported on GPUs with compute capability of at least 8.0. Your NVIDIA TITAN RTX GPU has compute capability 7.5. You can use float16 instead by explicitly setting thedtype flag in CLI, for example: --dtype=half.