Check NVLink® in Windows

Please note that you need to Install NVIDIA® drivers in Windows or Install CUDA® toolkit in Windows before checking NVLink® connections. Also, Switch GPU mode to TCC.

Install Visual Studio

Let’s ensure everything is functioning correctly by runnig cuda-samples from the official repository. To accomplish this, we need to install Visual Studio 2022 CE (Community Edition) sequentially and reinstall CUDA® Toolkit to activate VS plugins. Visit https://visualstudio.microsoft.com/downloads/ to download the Visual Studio 2022:

Execute the downloaded installer, tick Desktop development with C++, and click the Install button:

Run tests

Re-install CUDA® toolkit using our step-by-step guide Install CUDA® toolkit in Windows. Reboot server and download ZIP-archive with cuda-samples. Unzip it and open the subdirectory Samples\1_Utilities\bandwidthTest. Double click on bandwidthTest_vs2022 and run using Ctrl + F5 keyboard shortcut:

[CUDA Bandwidth Test] - Starting...
  Running on...
  
   Device 0: NVIDIA RTX A6000
   Quick Mode
  
   Host to Device Bandwidth, 1 Device(s)
   PINNED Memory Transfers
     Transfer Size (Bytes)        Bandwidth(GB/s)
     32000000                     6.0
  
   Device to Host Bandwidth, 1 Device(s)
   PINNED Memory Transfers
     Transfer Size (Bytes)        Bandwidth(GB/s)
     32000000                     6.6
  
   Device to Device Bandwidth, 1 Device(s)
   PINNED Memory Transfers
     Transfer Size (Bytes)        Bandwidth(GB/s)
     32000000                     637.2
  
  Result = PASS
  
  NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

You can run any sample. Try Samples\5_Domain_Specific\p2pBandwidthLatencyTest to see your topology and connectivity matrix:

[P2P (Peer-to-Peer) GPU Bandwidth Latency Test]
  Device: 0, NVIDIA RTX A6000, pciBusID: 3, pciDeviceID: 0, pciDomainID:0
  Device: 1, NVIDIA RTX A6000, pciBusID: 4, pciDeviceID: 0, pciDomainID:0
  Device=0 CAN Access Peer Device=1
  Device=1 CAN Access Peer Device=0
  
  ***NOTE: In case a device doesn't have P2P access to other one, it falls back to normal memcopy procedure.
  So you can see lesser Bandwidth (GB/s) and unstable Latency (us) in those cases.
  
  P2P Connectivity Matrix
       D\D     0     1
       0       1     1
       1       1     1
  Unidirectional P2P=Disabled Bandwidth Matrix (GB/s)
     D\D     0      1
       0 671.38   6.06
       1   6.06 671.47
  Unidirectional P2P=Enabled Bandwidth (P2P Writes) Matrix (GB/s)
     D\D     0      1
       0 631.31  52.73
       1  52.83 673.00
  Bidirectional P2P=Disabled Bandwidth Matrix (GB/s)
     D\D     0      1
       0 645.00   8.19
       1   8.11 677.87
  Bidirectional P2P=Enabled Bandwidth Matrix (GB/s)
     D\D     0      1
       0 655.96 101.78
       1 101.70 677.92
  P2P=Disabled Latency Matrix (us)
     GPU     0      1
       0   2.20  49.07
       1  10.33   2.20
  
     CPU     0      1
       0   3.55   7.01
       1   6.79   3.39
  P2P=Enabled Latency (P2P Writes) Matrix (us)
     GPU     0      1
       0   2.19   1.33
       1   1.26   2.22
  
     CPU     0      1
       0   6.80   4.86
       1   2.09   3.02
  
  NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

See also:

• Install NVIDIA® drivers in Windows
• Install CUDA® toolkit in Windows
• GPU rendering in RDP