Merge pull request #1405 from janhq/docs/improve-gpu-not-used
docs: improve gpu not used guide
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Hieu
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a1e9e0e5f1
@ -11,107 +11,179 @@ keywords: [
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no-subscription fee,
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no-subscription fee,
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large language model,
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large language model,
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troubleshooting,
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troubleshooting,
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using GPU,
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]
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]
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---
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---
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## Requirements for running Jan App in GPU mode on Windows and Linux
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This guide provides steps to troubleshoot and resolve issues when Jan app does not utilize the GPU on Windows and Linux systems.
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- You must have an NVIDIA driver that supports CUDA 11.4 or higher. Refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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To check if the NVIDIA driver is installed, open PowerShell or Terminal and enter the following command:
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```bash
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nvidia-smi
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```
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If you see a result similar to the following, you have successfully installed the NVIDIA driver:
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```bash
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
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| 0% 51C P8 10W / 170W | 364MiB / 7982MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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```
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- You must have CUDA 11.4 or higher (refer [here](https://developer.nvidia.com/cuda-toolkit-archive)).
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## Requirements for Running Jan in GPU Mode on Windows and Linux
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To check if CUDA is installed, open PowerShell or Terminal and enter the following command:
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```bash
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nvcc --version
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```
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If you see a result similar to the following, you have successfully installed CUDA:
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```bash
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nvcc: NVIDIA (R) Cuda compiler driver
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Cuda compilation tools, release 11.4, V11.4.100
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### NVIDIA Driver
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Build cuda_11.4.r11.4/compiler.30033411_0
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```
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- Specifically for Linux:
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Ensure that you have installed the NVIDIA driver that supports CUDA 11.4 or higher. For a detailed of CUDA compatibility, please refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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- you must have `gcc-11`, `g++-11`, `cpp-11` or higher, refer [here](https://gcc.gnu.org/projects/cxx-status.html#cxx17). For Ubuntu, you can install g++ 11 by following the instructions [here](https://linuxconfig.org/how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa).
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```bash
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# Example for ubuntu
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# Add the following PPA repository
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sudo add-apt-repository ppa:ubuntu-toolchain-r/test
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# Update the package list
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sudo apt update
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# Install g++ 11
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sudo apt-get install -y gcc-11 g++-11 cpp-11
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# Update the default g++ version
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To verify, open PowerShell or Terminal and enter the following command:
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sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110 \
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```bash
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nvidia-smi
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```
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If you see a result similar to the following, you have successfully installed the NVIDIA driver:
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```bash
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
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| 0% 51C P8 10W / 170W | 364MiB / 7982MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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```
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### CUDA Toolkit
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Ensure that you have installed the CUDA toolkit that is compatible with your NVIDIA driver. For a detailed of CUDA compatibility, please refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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To verify, open PowerShell or Terminal and enter the following command:
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```bash
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nvcc --version
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```
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If you see a result similar to the following, you have successfully installed CUDA:
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```bash
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nvcc: NVIDIA (R) Cuda compiler driver
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Cuda compilation tools, release 11.4, V11.4.100
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Build cuda_11.4.r11.4/compiler.30033411_0
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```
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### Specific Requirements for Linux
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**GCC and G++ Version**: Ensure that you have installed `gcc-11`, `g++-11`, `cpp-11` or higher, refer [here](https://gcc.gnu.org/projects/cxx-status.html#cxx17). For Ubuntu, you can install g++ 11 by following the instructions [here](https://linuxconfig.org/how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa).
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```bash
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# Example for ubuntu
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# Add the following PPA repository
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sudo add-apt-repository ppa:ubuntu-toolchain-r/test
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# Update the package list
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sudo apt update
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# Install g++ 11
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sudo apt-get install -y gcc-11 g++-11 cpp-11
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# Update the default g++ version
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sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110 \
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--slave /usr/bin/g++ g++ /usr/bin/g++-11 \
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--slave /usr/bin/g++ g++ /usr/bin/g++-11 \
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--slave /usr/bin/gcov gcov /usr/bin/gcov-11 \
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--slave /usr/bin/gcov gcov /usr/bin/gcov-11 \
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--slave /usr/bin/gcc-ar gcc-ar /usr/bin/gcc-ar-11 \
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--slave /usr/bin/gcc-ar gcc-ar /usr/bin/gcc-ar-11 \
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--slave /usr/bin/gcc-ranlib gcc-ranlib /usr/bin/gcc-ranlib-11
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--slave /usr/bin/gcc-ranlib gcc-ranlib /usr/bin/gcc-ranlib-11
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sudo update-alternatives --install /usr/bin/cpp cpp /usr/bin/cpp-11 110
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sudo update-alternatives --install /usr/bin/cpp cpp /usr/bin/cpp-11 110
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# Check the default g++ version
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# Check the default g++ version
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g++ --version
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g++ --version
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```
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```
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- You must add the `.so` libraries of CUDA to the `LD_LIBRARY_PATH` environment variable, refer [here](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions).
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```bash
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# Example for ubuntu with CUDA 11.4
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sudo nano /etc/environment
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# Add /usr/local/cuda-11.4/bin to the PATH environment variable - the first line
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# Add the following line to the end of the file
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LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64
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# Save and exit
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**Post-Installation Actions**: You must add the `.so` libraries of CUDA to the `LD_LIBRARY_PATH` environment variable by following the [Post-installation Actions instruction](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions).
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# Restart your computer or log out and log in again, the changes will take effect
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```
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## How to switch mode CPU/GPU Jan app
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By default, Jan app will run in CPU mode. When starting Jan app, the program will automatically check if your computer meets the requirements to run in GPU mode. If it does, we will automatically enable GPU mode and pick the GPU has highest VGRAM for you (feature allowing users to select one or more GPU devices for use - currently in planning). You can check whether you are using CPU mode or GPU mode in the settings/advance section of Jan app. (see image below). 
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```bash
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# Example for ubuntu with CUDA 11.4
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sudo nano /etc/environment
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# Add /usr/local/cuda-11.4/bin to the PATH environment variable - the first line
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# Add the following line to the end of the file
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LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64
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If you have GPU mode but it is not enabled by default, the following possibilities may exist, you can follow the next steps to fix the error:
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# Save and exit
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# Restart your computer or log out and log in again, the changes will take effect
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```
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1. You have not installed the NVIDIA driver, refer to the NVIDIA driver that supports CUDA 11.4 [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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## Switching Between CPU/GPU Modes in Jan
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2. You have not installed the CUDA toolkit or your CUDA toolkit is not compatible with the NVIDIA driver, refer to CUDA compatibility [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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By default, Jan runs in CPU mode. Upon start, Jan checks if your system is capable of running in GPU mode. If compatible, GPU mode is enabled automatically, and the GPU with the highest VRAM is selected. This setting can be verified in the `Settings` > `Advanced` section.
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3. You have not installed a CUDA compatible driver, refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver), and you must add the `.so` libraries of CUDA and the CUDA compatible driver to the `LD_LIBRARY_PATH` environment variable, refer [here](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions). For Windows, add the `.dll` libraries of CUDA and the CUDA compatible driver to the `PATH` environment variable. Usually, when installing CUDA on Windows, this environment variable is automatically added, but if you do not see it, you can add it manually by referring [here](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#environment-setup).
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## To check the current GPU-related settings that Jan app has detected, you can go to the Settings/Advanced section as shown in the image below.
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If you find that GPU mode is available but not enabled by default, consider the following troubleshooting steps:
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When you have an issue with GPU mode, share the `settings.json` with us will help us to solve the problem faster.
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:::tip
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## Tested on
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1. Check if you have installed the NVIDIA driver that supports CUDA 11.4 or higher. For a detailed of CUDA compatibility, please refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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2. Ensure that the CUDA toolkit is installed and compatible with your NVIDIA driver. For a detailed of CUDA compatibility, please refer [here](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver).
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3. For Linux, it's crucial to add the `.so` libraries of CUDA and the CUDA compatible driver to the `LD_LIBRARY_PATH` environment variable. For Windows, users should ensure that the `.dll` libraries of CUDA and the CUDA compatible driver are included in the PATH environment variable. Usually, when installing CUDA on Windows, this environment variable is automatically added, but if you do not see it, you can add it manually by referring [here](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#environment-setup).
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:::
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## Checking GPU Settings in Jan
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1. To check the current GPU settings detected by Jan, navigate to `Settings` > `Advanced` > `Open App Directory`
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<br></br>
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<br></br>
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2. Open the `settings.json` file under the `settings` folder. The following is an example of the `settings.json` file:
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<br></br>
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```json title="~/jan/settings/settings.json"
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{
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"notify": true,
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"run_mode": "gpu",
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"nvidia_driver": {
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"exist": true,
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"version": "531.18"
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},
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"cuda": {
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"exist": true,
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"version": "12"
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},
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"gpus": [
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{
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"id": "0",
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"vram": "12282"
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},
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{
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"id": "1",
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"vram": "6144"
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},
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{
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"id": "2",
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"vram": "6144"
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}
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],
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"gpu_highest_vram": "0"
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}
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```
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:::tip
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Troubleshooting tips:
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- Ensure the `nvidia_driver` and `cuda` fields indicate that requirements software are installed.
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- If the `gpus` field is empty or does not list your GPU, verify the installation of the NVIDIA driver and CUDA toolkit.
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- For further assistance, please share the `settings.json` with us.
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:::
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## Tested Configurations
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- Windows 11 Pro 64-bit, NVIDIA GeForce RTX 4070ti GPU, CUDA 12.2, NVIDIA driver 531.18 (Bare metal)
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- Windows 11 Pro 64-bit, NVIDIA GeForce RTX 4070ti GPU, CUDA 12.2, NVIDIA driver 531.18 (Bare metal)
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- Ubuntu 22.04 LTS, NVIDIA GeForce RTX 4070ti GPU, CUDA 12.2, NVIDIA driver 545 (Bare metal)
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- Ubuntu 22.04 LTS, NVIDIA GeForce RTX 4070ti GPU, CUDA 12.2, NVIDIA driver 545 (Bare metal)
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- Ubuntu 18.04 LTS, NVIDIA GeForce GTX 1660ti GPU, CUDA 12.1, NVIDIA driver 535 (Proxmox VM passthrough GPU)
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- Ubuntu 20.04 LTS, NVIDIA GeForce GTX 1660ti GPU, CUDA 12.1, NVIDIA driver 535 (Proxmox VM passthrough GPU)
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- Ubuntu 20.04 LTS, NVIDIA GeForce GTX 1660ti GPU, CUDA 12.1, NVIDIA driver 535 (Proxmox VM passthrough GPU)
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- Ubuntu 18.04 LTS, NVIDIA GeForce GTX 1660ti GPU, CUDA 12.1, NVIDIA driver 535 (Proxmox VM passthrough GPU)
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## Common Issues and Solutions
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## If you are experiencing this issue
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1. If the issue persists, please install the [Nightly version](/install/nightly) instead.
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1. If the issue persists, please install the [Nightly version](/install/nightly) instead.
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1. If the issue persists, ensure your (V)RAM is accessible by the application. Some folks have virtual RAM and needs additional configuration.
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2. If the issue persists, ensure your (V)RAM is accessible by the application. Some folks have virtual RAM and need additional configuration.
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1. Get help in [Jan Discord](https://discord.gg/mY69SZaMaC).
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3. Get help in [Jan Discord](https://discord.gg/mY69SZaMaC).
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Block a user