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# Installation
- **With Cuda support**:
1. First, make sure that Cuda is correctly installed.
- `nvcc --version` should print your information about your Cuda compiler driver.
- `nvidia-smi --query-gpu=compute_cap --format=csv` should print your GPUs compute capability, e.g. something
like:
```bash
compute_cap
8.9
```
If any of the above commands errors out, please make sure to update your Cuda version.
2. Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) with Cuda support
Start by creating a new cargo:
```bash
cargo new myapp
cd myapp
```
Make sure to add the `candle-core` crate with the cuda feature:
```
cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
```
Run `cargo build` to make sure everything can be correctly built.
```bash
cargo run
```
**Without Cuda support**:
Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as follows:
```bash
cargo new myapp
cd myapp
cargo add --git https://github.com/huggingface/candle.git candle-core
```
Finally, run `cargo build` to make sure everything can be correctly built.
```
cargo run
```
**With mkl support**
You can also see the `mkl` feature which could be interesting to get faster inference on CPU. [Using mkl](./advanced/mkl.md)
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