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# Installation
-Start by creating a new app:
+**With Cuda support**:
+
+1. First, make sure that Cuda is correctly installed.
+- `nvcc --version` should print 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
-cargo add --git https://github.com/huggingface/candle.git candle-core
```
-At this point, candle will be built **without** CUDA support.
-To get CUDA support use the `cuda` feature
+Make sure to add the `candle-core` crate with the cuda feature:
+
+```bash
+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 add --git https://github.com/huggingface/candle.git candle-core --features cuda
+cargo build
+```
+
+**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
```
-You can check everything works properly:
+Finally, run `cargo build` to make sure everything can be correctly built.
```bash
cargo build
```
+**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)