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1. #WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 DRIVERS#
2. #WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 PRO#
3. #WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 TV#
4. #WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 FREE#

I deleted the entire environment and created it again.

#WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 DRIVERS#

I’ve removed the drivers and performed a fresh install.

#WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 FREE#

Why are there only 1.85 MB free when the capacity is 8 GB and only 6.32 GB have been allocated?.

Tried to allocate 1024.00 KiB (GPU 0 8.00 GiB total capacity 6.32 GiB already allocated 1.85 MiB free 0 bytes cached) If I uncomment the first n_2070 = int(1.69605e9), I get the following mind-boggling error: Therefore, the 1060 used all its memory while the 2070 has more than 1 GB left. Maximum GPU cached memory usage on GeForce GTX 1060 6GB: 5.10001152 GBĬurrent GPU cached memory usage on GeForce GTX 1060 6GB: 5.10001152 GBĪccording to GPU-Z, the 2070 now has 6831 MB occupied and the 10 MB occupied. Maximum GPU memory usage by tensors on GeForce GTX 1060 6GB: 5.10001152 GBĬurrent GPU memory usage by tensors on GeForce GTX 1060 6GB: 0.0 GB Maximum GPU cached memory usage on GeForce RTX 2070: 6.785073152 GBĬurrent GPU cached memory usage on GeForce RTX 2070: 6.785073152 GB Maximum GPU memory usage by tensors on GeForce RTX 2070: 6.784156672 GBĬurrent GPU memory usage by tensors on GeForce RTX 2070: 6.784024576 GB Print(f"CUDA capability GB")Įstimated tensor size for 1060: 5.100 GB torch.empty((n_1060, 1), device=device_1060) import torchĭevice_name = _device_name(device) The 1060 is the primary card and usually has about 810 MB RAM occupied.

I’m printing some CUDA memory metrics but since I’m fairly (make that extremely) new to this I’m also monitoring GPU memory usage on both cards using GPU-Z. The approach was to find the maximum tensor size that fits on each card and see if I can fill the RAM memory this way. I’ve replicated the problem on both the nightly and stable versions of PyTorch 1.0 CUDA 10. To make the example below clearer, let me just mention that I also have a GTX 1060 6 GB and I’m running Windows 10. This way I can precisely check how much memory I can use. I made a tiny Jupyter notebook in which I create tensors of a given size on the GPU. I’ve noticed this while running models but realized I need a more objective way to test it.

#WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 TV#

TV Gamer: i7-8700K 5.0ghz All-core delidded // Deepcool Gamerstorm Assassin III // Gigabyte Aorus Z370 Gaming 5 // 32GB (4x8) Corsair Vengeance LPX DDR4 3200 // XFX 5700XT RAW II // Corsair 275R Airflow // Corsair 650M Vengeance 650w // Intel 660P 1TB NVME M.I just got a brand new RTX 2070 8 GB and while it’s certainly fast, I don’t seem to be able to utilize its entire memory capacity.

#WINDOWS 7 MEMORY MONITOR AT 7GB OUT OF 8 PRO#

Wife's System: i9-9900K Stock // Cryorig H7 Quad Lumi // Gigabyte Z390M Gaming // 32GB (4x8) Corsair Vengeance LED DDR4 3200 // ASUS KO RTX 3070 // Cooler Master Master Box NR400 ODD // Seasonic Focus Plus Gold 850w // 1TB ADATA XPG SX8200 Pro/1TB SAMSUNG 860 EVO/4TB Western Digital HDD // Displays: LG Ultragear 27GL83A-B/AOC AGON AG241QX/ASUS VG248QE // Glorious GMMK TKL // Logitech G502 Hero // Corsair Void Pro RGB / / LG BDRW / / NexStar 5.25" USB 3 Enclosure My System: i9-10900KF 5.1-5.3ghz 1.375v // Corsair iCUE H150i Elite Capellix // Gigabyte Z590 Aorus Elite AX // 32GB (4x8) Crucial Ballistix Elite DDR4 4000/CL18 // ASUS RTX 3080 TUF OC // Corsair 5000D Airflow // Corsair SP120 RGB Pro x7 // Seasonic Focus Plus Gold 850w //1TB ADATA XPG SX8200 Pro/1TB Teamgroup MP33/2TB Seagate 7200RPM Hard Drive // Displays: LG Ultragear 32GP83B /Lenovo L24Q-30/Lenovo L24Q-30 // Glorious GMMK TKL // Logitech G502 Hero // Corsair Void Pro RGB