File size: 7,149 Bytes
7a1ad33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # Manual Local Model Routing Setup (experimental)
Gemini CLI supports using a local model for
[routing decisions](../cli/model-routing.md). When configured, Gemini CLI will
use a locally-running **Gemma** model to make routing decisions (instead of
sending routing decisions to a hosted model).
<!-- prettier-ignore -->
> [!NOTE]
> This is an experimental feature currently under active development.
<!-- prettier-ignore -->
> [!IMPORTANT]
> **Recommended:** We now provide a fully automated setup command. We recommend
> using the [`gemini gemma` Setup Guide](./gemma-setup.md) instead of following
> these manual steps.
This feature can help reduce costs associated with hosted model usage while
offering similar routing decision latency and quality.
## Manual Setup
Using a Gemma model for routing decisions requires that an implementation of a
Gemma model be running locally on your machine, served behind an HTTP endpoint
and accessed via the Gemini API. If you cannot use the `gemini gemma setup`
command, follow these manual steps:
### Download the LiteRT-LM runtime
The [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM) runtime offers
pre-built binaries for locally-serving models. Download the binary appropriate
for your system.
#### Windows
1. Download
[lit.windows_x86_64.exe](https://github.com/google-ai-edge/LiteRT-LM/releases/download/v0.9.0-alpha03/lit.windows_x86_64.exe).
2. Using GPU on Windows requires the DirectXShaderCompiler. Download the
[dxc zip from the latest release](https://github.com/microsoft/DirectXShaderCompiler/releases/download/v1.8.2505.1/dxc_2025_07_14.zip).
Unzip the archive and from the architecture-appropriate `bin\` directory, and
copy the `dxil.dll` and `dxcompiler.dll` into the same location as you saved
`lit.windows_x86_64.exe`.
3. (Optional) Test starting the runtime:
`.\lit.windows_x86_64.exe serve --verbose`
#### Linux
1. Download
[lit.linux_x86_64](https://github.com/google-ai-edge/LiteRT-LM/releases/download/v0.9.0-alpha03/lit.linux_x86_64).
2. Ensure the binary is executable: `chmod a+x lit.linux_x86_64`
3. (Optional) Test starting the runtime: `./lit.linux_x86_64 serve --verbose`
#### MacOS
1. Download
[lit-macos-arm64](https://github.com/google-ai-edge/LiteRT-LM/releases/download/v0.9.0-alpha03/lit.macos_arm64).
2. Ensure the binary is executable: `chmod a+x lit.macos_arm64`
3. (Optional) Test starting the runtime: `./lit.macos_arm64 serve --verbose`
> **Note**: MacOS can be configured to only allows binaries from "App Store &
> Known Developers". If you encounter an error message when attempting to run
> the binary, you will need to allow the application. One option is to visit
> `System Settings -> Privacy & Security`, scroll to `Security`, and click
> `"Allow Anyway"` for `"lit.macos_arm64"`. Another option is to run
> `xattr -d com.apple.quarantine lit.macos_arm64` from the commandline.
### Download the Gemma Model
Before using Gemma, you will need to download the model (and agree to the Terms
of Service).
This can be done via the LiteRT-LM runtime.
#### Windows
```bash
$ .\lit.windows_x86_64.exe pull gemma3-1b-gpu-custom
[Legal] The model you are about to download is governed by
the Gemma Terms of Use and Prohibited Use Policy. Please review these terms and ensure you agree before continuing.
Full Terms: https://ai.google.dev/gemma/terms
Prohibited Use Policy: https://ai.google.dev/gemma/prohibited_use_policy
Do you accept these terms? (Y/N): Y
Terms accepted.
Downloading model 'gemma3-1b-gpu-custom' ...
Downloading... 968.6 MB
Download complete.
```
#### Linux
```bash
$ ./lit.linux_x86_64 pull gemma3-1b-gpu-custom
[Legal] The model you are about to download is governed by
the Gemma Terms of Use and Prohibited Use Policy. Please review these terms and ensure you agree before continuing.
Full Terms: https://ai.google.dev/gemma/terms
Prohibited Use Policy: https://ai.google.dev/gemma/prohibited_use_policy
Do you accept these terms? (Y/N): Y
Terms accepted.
Downloading model 'gemma3-1b-gpu-custom' ...
Downloading... 968.6 MB
Download complete.
```
#### MacOS
```bash
$ ./lit.lit.macos_arm64 pull gemma3-1b-gpu-custom
[Legal] The model you are about to download is governed by
the Gemma Terms of Use and Prohibited Use Policy. Review these terms and ensure you agree before continuing.
Full Terms: https://ai.google.dev/gemma/terms
Prohibited Use Policy: https://ai.google.dev/gemma/prohibited_use_policy
Do you accept these terms? (Y/N): Y
Terms accepted.
Downloading model 'gemma3-1b-gpu-custom' ...
Downloading... 968.6 MB
Download complete.
```
### Start LiteRT-LM Runtime
Using the command appropriate to your system, start the LiteRT-LM runtime.
Configure the port that you want to use for your Gemma model. For the purposes
of this document, we will use port `9379`.
Example command for MacOS: `./lit.macos_arm64 serve --port=9379 --verbose`
### (Optional) Verify Model Serving
Send a quick prompt to the model via HTTP to validate successful model serving.
This will cause the runtime to download the model and run it once.
You should see a short joke in the server output as an indicator of success.
#### Windows
```
# Run this in PowerShell to send a request to the server
$uri = "http://localhost:9379/v1beta/models/gemma3-1b-gpu-custom:generateContent"
$body = @{contents = @( @{
role = "user"
parts = @( @{ text = "Tell me a joke." } )
})} | ConvertTo-Json -Depth 10
Invoke-RestMethod -Uri $uri -Method Post -Body $body -ContentType "application/json"
```
#### Linux/MacOS
```bash
$ curl "http://localhost:9379/v1beta/models/gemma3-1b-gpu-custom:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{"contents":[{"role":"user","parts":[{"text":"Tell me a joke."}]}]}'
```
## Configuration
To use a local Gemma model for routing, you must explicitly enable it in your
`settings.json`:
```json
{
"experimental": {
"gemmaModelRouter": {
"enabled": true,
"classifier": {
"host": "http://localhost:9379",
"model": "gemma3-1b-gpu-custom"
}
}
}
}
```
> Use the port you started your LiteRT-LM runtime on in the setup steps.
### Configuration schema
| Field | Type | Required | Description |
| :----------------- | :------ | :------- | :----------------------------------------------------------------------------------------- |
| `enabled` | boolean | Yes | Must be `true` to enable the feature. |
| `classifier` | object | Yes | The configuration for the local model endpoint. It includes the host and model specifiers. |
| `classifier.host` | string | Yes | The URL to the local model server. Should be `http://localhost:<port>`. |
| `classifier.model` | string | Yes | The model name to use for decisions. Must be `"gemma3-1b-gpu-custom"`. |
> **Note: You will need to restart after configuration changes for local model
> routing to take effect.**
|