nvidia-smi gpu not supported

如果您尝试设置不支持的显卡频率，nvidia-smi会显示一个有用的提示信息。但是需要注意的是，GPU Boost的设置在重启或驱动程序卸载后不会被保留，如果需要持续有效，可以使用脚本进行设置。除非使用nvidia-smi -pm 1命令设置了NVIDIA驱动程序的持续模式，否则当GPU处于空闲状态时，驱动程序可能会卸载。

相关问题

RuntimeError: CUDA error: API call is not supported in the installed CUDA driver

这个错误是由于安装的CUDA驱动程序不支持所调用的API导致的。为了解决这个问题，你可以尝试几个步骤：

1. 确保你的CUDA驱动程序已正确安装。可以通过运行`nvidia-smi`命令来检查CUDA驱动程序的版本和状态。

2. 如果CUDA驱动程序已安装，但仍然出现这个错误，可能是因为你的CUDA版本与你使用的软件不兼容。你可以尝试降低或升级CUDA版本，以解决兼容性问题。

3. 如果你正在使用的是GPU加速的软件，可以尝试禁用GPU加速，以避免与CUDA驱动程序的兼容性问题。具体操作取决于你使用的软件，可以在软件的设置中查找相关选项。

4. 如果以上方法都无效，你可以尝试更新或重新安装CUDA驱动程序。确保选择与你的显卡型号和操作系统兼容的最新版本。

希望以上方法能够帮助你解决这个问题。如果问题仍然存在，请提供更多详细信息，以便我能够更好地帮助你解决。

deepseek gpu CUDA

### DeepSeek GPU CUDA Configuration and Compatibility

For applications like DeepSeek that require significant computational resources, the choice of GPU and CUDA version is critical to ensure optimal performance. Applications with high parallelism can leverage GPUs' massive parallel architecture to achieve higher performance compared to CPUs[^1].

When configuring a system for DeepSeek:

- **GPU Selection**: Opting for NVIDIA GPUs designed specifically for deep learning tasks such as Tesla, Quadro, or GeForce RTX series would be beneficial due to their superior compute capabilities.

- **CUDA Version Matching**: It's important to match the CUDA toolkit version used during development closely with what’s supported by both the chosen GPU model and the operating environment. Discrepancies between `nvidia-smi` reported versions versus those shown via `nvcc --version` might occur but generally do not pose severe issues unless there are major differences in minor release numbers[^2].

To install an appropriate CUDA Toolkit suitable for running DeepSeek efficiently, one should visit the official NVIDIA website archive page where different versions of CUDA Toolkits along with installation guides are available[^3]:

wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run

Ensure environmental variables point correctly towards newly installed CUDA directories after setup completes.

--related questions--

1. What specific models within the Tesla line offer best support for deep learning frameworks?
2. How does selecting between Runtime API vs Driver API impact application deployment timelines when working on projects similar to DeepSeek?
3. Are there any particular considerations regarding memory management while optimizing code intended for execution on NVIDIA hardware?