OLLAMA_HOST=https://<name>.fly.dev ollama run orca-mini:3b
```
`shared-cpu-8x` is a free-tier eligible machine type. For better performance, switch to a `performance` or `dedicated` machine type or attach a GPU for hardware acceleration (see below).
## (Optional) Persistent Volume
By default Fly Machines use ephemeral storage which is problematic if you want to use the same model across restarts without pulling it again. Create and attach a persistent volume to store the downloaded models:
1. Create the Fly Volume
```bash
fly volume create ollama
```
1. Update `fly.toml` and add `[mounts]`
```toml
[mounts]
source = "ollama"
destination = "/mnt/ollama/models"
```
1. Update `fly.toml` and add `[env]`
```toml
[env]
OLLAMA_MODELS = "/mnt/ollama/models"
```
1. Deploy your app
```bash
fly deploy
```
## (Optional) Hardware Acceleration
Fly.io GPU is currently in waitlist. Sign up for the waitlist: https://fly.io/gpu
Once you've been accepted, create the app with the additional flags `--vm-gpu-kind a100-pcie-40gb` or `--vm-gpu-kind a100-pcie-80gb`.
This example downloads and installs Ollama in a Jupyter instance such as Google Colab. It will start the Ollama service and expose an endpoint using `ngrok` which can be used to communicate with the Ollama instance remotely.
For best results, use an instance with GPU accelerator.
"The previous cell starts two processes, `ollama` and `ngrok`. The log output will show a line like the following which describes the external address.\n",
- Kubernetes cluster. This example will use Google Kubernetes Engine.
## Steps
1. Create the Ollama namespace, deployment, and service
```bash
kubectl apply -f cpu.yaml
```
## (Optional) Hardware Acceleration
Hardware acceleration in Kubernetes requires NVIDIA's [`k8s-device-plugin`](https://github.com/NVIDIA/k8s-device-plugin) which is deployed in Kubernetes in form of daemonset. Follow the link for more details.
Once configured, create a GPU enabled Ollama deployment.
```bash
kubectl apply -f gpu.yaml
```
## Test
1. Port forward the Ollama service to connect and use it locally