Windows 11 does not see my 2nd GPU (4090 + 4080)

AIGUYCONTENT · May 19, 2024, 5:08pm

I have the following rig for AI:

Gigabyte 4090
MSI MAG X670E Tomahawk WiFI
7800x3d
RM1000x — 1000 Watt 80 PLUS® Gold
64 GB DDR5 @ 6000mhz
Fractal XL case

The motherboard has the following PCIe slots:

PCI_E1: PCIe 5.0 x16 (From CPU)
PCI_E2: PCIe 3.0 x1 (From X670 Chipset)
PCI_E3: PCIe 4.0 x4 (From CPU)
PCI_E4: PCIe 4.0 x2 (From X670 Chipset)

Here’s the manual: https://download.msi.com/archive/mnu_exe/mb/MAGX670ETOMAHAWKWIFI.pdf

I have a Gigabyte 4080 that’s sitting around collecting dust in another computer. I want to add the 4080 to my current rig to increase VRAM (16GB 4080 + 24 GB 4090 = 40GB) and hopefully token/second speeds.

The 4090 sits in the PCI_E1: PCIe 5.0 x16 (from CPU) slot on the MSI X670E MAG motherboard. It’s so fat that it takes up three slots. The only other available slot to mount the 4080 is the PCI_E4: PCIe 4.0 x2 (from X670 chipset).

I plugged the 4080 into the PCI_E4: PCIe 4.0 x2 (from X670 chipset) slot via a riser cable. I’m using an external Corsair 850W power supply to power the 4080 (and nothing else).

The problem is that Windows Device Manager does not recognize nor see the 4080.

I have already tried the following:

Swapped 4080 and 4090. The system does see the 4080. So, the card is good.
Changed the BIOS to x8+x8 (Windows still did not recognize it). Changed it again to x4+x4+x4+x4 and it did not see it.
Performed an hour of Google searching to no avail
Changed from “Auto” to “Gen3” in the BIOS
Ensure all cables/etc are plugged in properly.
Had the Device Manager search for new hardware (none found). Showed “hidden” items and the 4080 was not found.
A few other online “tips” that I found while browsing Reddit etc.

The only solution I can think of is to get a second PCIe riser cable and plug the 4090 into the PCI_E1: PCIe 5.0 x16 (from CPU) slot via riser cable. Then plug the 4080 into the PCI_E3: PCIe 4.0 x4 (From CPU) slot.

Thank you if you can help me figure this out.

nephepritou · October 3, 2024, 6:10am

Did you solved an issue? Maybe tried to put 4090 to PCI_E3 and 4080 to PCI_E1? I want to do same setup and need to know the truth

John6666 · October 3, 2024, 7:48am

Did you try changing the ACS and IOMMU settings in the BIOS (UEFI)?
Well, if Windows itself does not recognize the GPU, it might be more of a hardware issue…

github.com/pytorch/pytorch

Multi-gpu example freeze and is not killable

opened 01:27PM - 09 Aug 19 UTC

Dubrzr

module: dependency bug module: multi-gpu module: multiprocessing module: cuda triaged module: deadlock has workaround module: data parallel quansight-nack

## 🐛 Bug Running pytorch with multiple P40 gpus freeze and is not killable (e…ven kill -9 by root). Only a reboot removes this process. Inside docker container (with nvidia-docker2) it freezes docker. https://github.com/NVIDIA/nvidia-docker/issues/1010 ## To Reproduce Steps to reproduce the behavior: 1. Install pytorch 1.0.2 2. Run the following code on multiple P40 Gpus ``` import os ###tutorial from https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html ###no error with only 1 gpu # os.environ['CUDA_VISIBLE_DEVICES'] = '0' #### to reproduce error allow multi gpu os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3' import torch torch.cuda.device_count() import torch.nn as nn from torch.utils.data import Dataset, DataLoader # Parameters and DataLoaders input_size = 5000 #increased input size (works with 500 on multi gpu) output_size = 2000 #increased output size (works with 200 on multi gpu) batch_size = 300 data_size = 100 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) class RandomDataset(Dataset): def __init__(self, size, length): self.len = length self.data = torch.randn(length, size) def __getitem__(self, index): return self.data[index] def __len__(self): return self.len rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size), batch_size=batch_size, shuffle=True) class Model(nn.Module): # Our model def __init__(self, input_size, output_size): super(Model, self).__init__() self.fc = nn.Linear(input_size, output_size) def forward(self, input): output = self.fc(input) return output model = Model(input_size, output_size) if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs model = nn.DataParallel(model) model.to(device) for i in range(10000): for data in rand_loader: input = data.to(device) output = model(input) ``` ## Expected behavior The training ## Environment Collecting environment information... PyTorch version: 1.0.1.post2 Is debug build: No CUDA used to build PyTorch: 10.0.130 OS: Ubuntu 18.04.2 LTS GCC version: (crosstool-NG fa8859cb) 7.2.0 CMake version: Could not collect Python version: 3.5 Is CUDA available: Yes CUDA runtime version: 10.0.130 GPU models and configuration: GPU 0: Tesla P40 GPU 1: Tesla P40 GPU 2: Tesla P40 GPU 3: Tesla P40 GPU 4: Tesla P40 GPU 5: Tesla P40 GPU 6: Tesla P40 GPU 7: Tesla P40 Nvidia driver version: 410.79 cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.4.2 Versions of relevant libraries: [pip3] numpy==1.15.2 [conda] mkl 2018.0.3 1 defaults [conda] mkl_fft 1.0.6 py35_0 conda-forge [conda] mkl_random 1.0.1 py35_0 conda-forge [conda] nomkl 2.0 0 defaults [conda] numexpr 2.6.5 py35_nomklhaa809a4_0 [nomkl] defaults [conda] pytorch 1.0.1 py3.5_cuda10.0.130_cudnn7.4.2_2 pytorch [conda] torch 0.4.1 <pip> [conda] torchvision 0.2.2 py_3 pytorch cc @ezyang @gchanan @zou3519 @ngimel