diff --git a/packages/coding-agent/docs/docs.json b/packages/coding-agent/docs/docs.json
index 4382848de..c9df247a3 100644
--- a/packages/coding-agent/docs/docs.json
+++ b/packages/coding-agent/docs/docs.json
@@ -47,6 +47,10 @@
               "title": "Run Local Models",
               "path": "llama-cpp.md"
             },
+            {
+              "title": "Run Local Models on NVIDIA RTX",
+              "path": "nvidia-rtx-llama-cpp.md"
+            },
             {
               "title": "Configure Your Terminal",
               "path": "terminal-setup.md"
diff --git a/packages/coding-agent/docs/llama-cpp.md b/packages/coding-agent/docs/llama-cpp.md
index ea51675e0..d4c41ab2e 100644
--- a/packages/coding-agent/docs/llama-cpp.md
+++ b/packages/coding-agent/docs/llama-cpp.md
@@ -41,7 +41,7 @@ A single-file model can sit directly in the model directory. Put multimodal and
     └── large-model-Q4_K_M-00003-of-00003.gguf
 ```
 
-Restart the router after manually adding files. For per-model context sizes and other options, use [llama.cpp model presets](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md#model-presets).
+Restart the router after manually adding files. For per-model context sizes and other options, use [llama.cpp model presets](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md#model-presets). For settings tuned to a specific NVIDIA GPU and model, see [Run Pi with NVIDIA RTX and llama.cpp](nvidia-rtx-llama-cpp.md).
 
 ## Configure Pi
 
diff --git a/packages/coding-agent/docs/nvidia-rtx-llama-cpp.md b/packages/coding-agent/docs/nvidia-rtx-llama-cpp.md
new file mode 100644
index 000000000..6a6163034
--- /dev/null
+++ b/packages/coding-agent/docs/nvidia-rtx-llama-cpp.md
@@ -0,0 +1,97 @@
+# Run Pi with NVIDIA RTX and llama.cpp
+
+[NVIDIA Local AI Inference Recipes](https://nvidia.github.io/local-ai-inference-recipes/index.html) provides llama.cpp settings tuned for specific NVIDIA hardware and models. Each recipe gives a single-model `llama-server` command. Pi works with the llama.cpp router, which can serve and manage several models, so this guide converts the recipe command into a router preset.
+
+The example below uses Windows PowerShell. See [Run Pi on Windows](windows.md) for native Windows and WSL setup, and [Local Models with llama.cpp](llama-cpp.md) for Pi's complete router and model-management reference.
+
+## Choose a recipe
+
+Open the [recipe catalog](https://nvidia.github.io/local-ai-inference-recipes/index.html), select your hardware, operating system, and model, then choose **llama.cpp**. Check the prerequisites and memory requirements. Keep the recipe's launch command for the conversion below.
+
+## Install llama.cpp
+
+Follow the recipe's setup instructions to install llama.cpp. Use a current build with router and model-preset support.
+
+## Create a model preset
+
+A preset lists the settings for one or more models. Each section name becomes the model ID shown in Pi. Save the preset next to `llama-server`, or pass its full path when starting the router.
+
+Convert each option from the recipe command into one preset line:
+
+- Remove the leading dashes: `--ctx-size 65536` becomes `ctx-size = 65536`.
+- Write options without a value as `true`: `--jinja` becomes `jinja = true`.
+- Remove quotes and PowerShell line-continuation backticks around values.
+- Leave out `--host` and `--port`. They belong on the router command line.
+- Add `load-on-startup = true` so the router loads the model when it starts.
+- Replace short model-file options with their long preset keys:
+
+| Recipe option | Preset key |
+|---|---|
+| `-hf` | `hf-repo` |
+| `-hff` | `hf-file` |
+| `-hfrd` | `hf-repo-draft` |
+| `--spec-draft-model` | `spec-draft-model` |
+
+For example, this recipe command:
+
+```powershell
+llama-server.exe -hf "unsloth/Qwen3.8-27B-GGUF" `
+  -hff "Qwen3.8-27B-UD-Q4_K_M.gguf" `
+  -hfrd "z-lab/Qwen3.8-27B-DFlash2-GGUF" `
+  --spec-draft-model "Qwen3.8-27B-DFlash2-Q4_K_M.gguf" `
+  --no-mmproj `
+  --host 127.0.0.1 `
+  --port 9931 `
+  --batch-size 4096 `
+  --ubatch-size 1024 `
+  --ctx-size 65536 `
+  --parallel 1 `
+  -ngl 999 `
+  --jinja `
+  --chat-template-kwargs '{"preserve_thinking":true}' `
+  --spec-type draft-dflash `
+  --spec-draft-n-max 7 `
+  --spec-draft-backend-sampling
+```
+
+becomes `qwen3.8-27b.ini`:
+
+```ini
+version = 1
+
+[qwen3.8-27b]
+load-on-startup = true
+hf-repo = unsloth/Qwen3.8-27B-GGUF
+hf-file = Qwen3.8-27B-UD-Q4_K_M.gguf
+hf-repo-draft = z-lab/Qwen3.8-27B-DFlash2-GGUF
+spec-draft-model = Qwen3.8-27B-DFlash2-Q4_K_M.gguf
+no-mmproj = true
+batch-size = 4096
+ubatch-size = 1024
+ctx-size = 65536
+parallel = 1
+ngl = 999
+jinja = true
+chat-template-kwargs = {"preserve_thinking":true}
+spec-type = draft-dflash
+spec-draft-n-max = 7
+spec-draft-backend-sampling = true
+```
+
+## Start the router
+
+Open PowerShell in the llama.cpp folder and run:
+
+```powershell
+.\llama-server.exe --models-preset qwen3.8-27b.ini --host 127.0.0.1 --port 8080
+```
+
+If the recipe requires a Hugging Face token, set `$env:HF_TOKEN` in the same PowerShell window before starting the router. On its first start, the router downloads the model files.
+
+Use the [router health checks](llama-cpp.md#troubleshooting) to confirm that the server is ready. In Windows PowerShell, use `curl.exe` rather than `curl`, which PowerShell maps to a different command. The model is ready when `/models` lists `qwen3.8-27b` with the status `loaded`.
+
+## Connect Pi
+
+Follow [Configure Pi](llama-cpp.md#configure-pi) and connect to `http://127.0.0.1:8080`. The preset section name is the model ID, so select `qwen3.8-27b` in `/model` for this example.
+
+NVIDIA recipe memory settings assume that only the selected model uses the GPU. If `/llama` shows other loaded models, unload them before using the recipe model.