diff --git a/docs/developer-guide/index.md b/docs/developer-guide/index.md
index db8d483..1628285 100644
--- a/docs/developer-guide/index.md
+++ b/docs/developer-guide/index.md
@@ -18,7 +18,7 @@ as private model upload and fine-tuning.
 - **Chat UI**
 - **Hardware Abstraction Layer (HAL)**
 - **CVM Management**
-- **Private Model Upload**
+- **Private Model Upload** - Deploy custom models via HAL build-time config, cloud-init, or runtime upload
 - **Fine-Tuning Models**
 - **Guardrails** - AI safety controls for input validation and output sanitization
 
diff --git a/docs/developer-guide/private-model-upload.md b/docs/developer-guide/private-model-upload.md
index c20afc8..0315231 100644
--- a/docs/developer-guide/private-model-upload.md
+++ b/docs/developer-guide/private-model-upload.md
@@ -6,24 +6,284 @@ sidebar_position: 3
 
 ## Uploading Private Models to Cube AI
 
-This guide explains how to upload private models into the Ollama runtime inside a confidential VM.
+This guide explains how to upload and deploy private or custom models into a Cube AI Confidential VM (CVM). Private models are models that are not available in public registries (Ollama library, HuggingFace) — for example, fine-tuned models, proprietary weights, or models with restricted access.
 
-## 1. Package Model Files
+### Port Reference
+
+CVM network access uses QEMU user-mode port forwarding. The following host-to-guest port mappings are configured in the QEMU launch scripts (`hal/buildroot/qemu.sh` and `hal/ubuntu/qemu.sh`):
+
+| Host Port | Guest Port | Service |
+| --- | --- | --- |
+| 6190 | 22 | SSH |
+| 6193 | 7001 | Cube Agent API |
+
+Inside the CVM, the LLM backends listen on their own ports (not directly exposed to the host):
+
+| Port | Service |
+| --- | --- |
+| 11434 | Ollama API |
+| 8000 | vLLM OpenAI-compatible API |
+
+The Cube Agent (port 7001 inside the CVM, 6193 on the host) acts as a reverse proxy to whichever LLM backend is configured, so all model inference requests go through the agent.
+
+---
+
+## Build-Time Model Embedding (Buildroot HAL)
+
+The Buildroot HAL supports embedding custom model configuration directly into the CVM image via `menuconfig`. This is the recommended approach for production deployments where models should be available immediately after boot.
+
+### Ollama
+
+#### Using menuconfig
+
+During HAL image configuration (see [HAL guide](/developer-guide/hal)), navigate to:
+
+**Target packages → Cube packages → ollama**
+
+Set the **Custom models to install** field with a space-separated list of Ollama model tags:
+
+```text
+llama2:7b mistral:7b codellama:13b
+```
+
+These models are pulled automatically on first boot by a script installed at `/usr/libexec/ollama/pull-models.sh`. 
+
+Or set it directly in the Buildroot defconfig:
 
 ```bash
-tar -czvf model-name.tar.gz /path/to/model/files
+BR2_PACKAGE_OLLAMA_CUSTOM_MODELS="llama2:7b mistral:7b codellama:13b"
 ```
 
-## 2. Transfer and Extract Model in CVM
+Then rebuild:
 
 ```bash
-scp model-name.tar.gz user@<cvm-ip>:~
-gunzip model-name.tar.gz
-tar -xvf model-name.tar
+make -j$(nproc)
 ```
 
-## 3. Copy Into Ollama
+#### Embedding GGUF Weights in the Image
+
+For models not available in the Ollama registry (e.g., your own fine-tuned GGUF weights), use the Buildroot filesystem overlay to embed the files directly:
+
+1. Place the model weights in the overlay:
 
 ```bash
-docker cp /path/to/extracted ollama:/models/
+mkdir -p cube/hal/buildroot/linux/board/cube/overlay/var/lib/ollama/custom-models/
+cp /path/to/weights.gguf cube/hal/buildroot/linux/board/cube/overlay/var/lib/ollama/custom-models/
+```
+
+2. Add a [Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md) to the overlay:
+
+```bash
+mkdir -p cube/hal/buildroot/linux/board/cube/overlay/etc/cube/modelfiles/
+cat > cube/hal/buildroot/linux/board/cube/overlay/etc/cube/modelfiles/my-model.Modelfile << 'EOF'
+FROM /var/lib/ollama/custom-models/weights.gguf
+PARAMETER temperature 0.7
+SYSTEM "You are a domain-specific assistant."
+EOF
+```
+
+3. Add a startup script in the overlay to register the model after Ollama starts:
+
+```bash
+mkdir -p cube/hal/buildroot/linux/board/cube/overlay/usr/libexec/ollama/
+cat > cube/hal/buildroot/linux/board/cube/overlay/usr/libexec/ollama/register-custom-models.sh << 'SCRIPT'
+#!/bin/sh
+# Wait for Ollama to be ready
+for i in $(seq 1 30); do
+  if curl -s http://localhost:11434/api/version > /dev/null 2>&1; then
+    break
+  fi
+  sleep 2
+done
+
+# Register custom models from Modelfiles
+for mf in /etc/cube/modelfiles/*.Modelfile; do
+  [ -f "$mf" ] || continue
+  name=$(basename "$mf" .Modelfile)
+  ollama create "$name" -f "$mf"
+done
+SCRIPT
+chmod +x cube/hal/buildroot/linux/board/cube/overlay/usr/libexec/ollama/register-custom-models.sh
+```
+
+4. Build the image:
+
+```bash
+cd buildroot
+make -j$(nproc)
+```
+
+### vLLM
+
+#### Using menuconfig
+
+Navigate to **Target packages → Cube packages → vllm** and set:
+
+- **Custom model path** — Absolute path to a HuggingFace-format model directory on your build machine
+
+Or in the defconfig:
+
+```bash
+BR2_PACKAGE_VLLM_CUSTOM_MODEL_PATH="/path/to/my-hf-model"
+```
+
+The build system copies the model files into `/var/lib/vllm/models/` in the image and configures vLLM to serve from that local path automatically. The vLLM service configuration is written to `/etc/vllm/vllm.env`.
+
+You can also configure inference parameters at build time:
+
+```bash
+BR2_PACKAGE_VLLM_MODEL="meta-llama/Llama-2-7b-hf"
+BR2_PACKAGE_VLLM_GPU_MEMORY="0.90"
+BR2_PACKAGE_VLLM_MAX_MODEL_LEN="2048"
+```
+
+Then rebuild:
+
+```bash
+make -j$(nproc)
+```
+
+---
+
+## Cloud-Init Model Provisioning (Ubuntu)
+
+For Ubuntu-based CVMs using cloud-init, custom models are configured in the `user-data` section of `hal/ubuntu/qemu.sh`. Models are provisioned during the first boot.
+
+### Ollama
+
+Add a Modelfile and registration commands to the `write_files` and `runcmd` sections of the cloud-init `user-data`:
+
+```yaml
+write_files:
+  - path: /etc/cube/modelfiles/my-model.Modelfile
+    content: |
+      FROM /var/lib/ollama/custom-models/weights.gguf
+      PARAMETER temperature 0.7
+      SYSTEM "You are a domain-specific assistant."
+    permissions: '0644'
+
+runcmd:
+  # ... (existing commands that install ollama and start it)
+  # Download private model weights from an internal server
+  - mkdir -p /var/lib/ollama/custom-models
+  - curl -o /var/lib/ollama/custom-models/weights.gguf https://internal-server/models/weights.gguf
+  # Wait for Ollama and register the custom model
+  - |
+    for i in $(seq 1 60); do
+      if curl -s http://localhost:11434/api/version > /dev/null 2>&1; then
+        break
+      fi
+      sleep 2
+    done
+    ollama create my-model -f /etc/cube/modelfiles/my-model.Modelfile
+```
+
+### vLLM
+
+Add a model download and vLLM configuration step to `runcmd`:
+
+```yaml
+runcmd:
+  - pip install vllm
+  - mkdir -p /var/lib/vllm/models
+  # Download from a private HuggingFace registry (requires token for gated models)
+  - |
+    HF_TOKEN="your-token-here"
+    huggingface-cli download my-org/my-private-model \
+      --local-dir /var/lib/vllm/models/my-private-model \
+      --token "$HF_TOKEN"
+  # Configure vLLM to use the downloaded model
+  - |
+    cat > /etc/vllm/vllm.env << 'ENVEOF'
+    VLLM_MODEL=/var/lib/vllm/models/my-private-model
+    VLLM_GPU_MEMORY_UTILIZATION=0.85
+    VLLM_MAX_MODEL_LEN=2048
+    ENVEOF
+  - systemctl restart vllm
+```
+
+---
+
+## Runtime Model Upload
+
+After a CVM is running (regardless of which approach was used to create it), you can deploy additional models over SSH.
+
+### Ollama
+
+#### 1. Transfer and Register
+
+```bash
+# Package model files on the host
+tar -czvf my-model.tar.gz /path/to/model/files
+
+# Copy into the CVM (port 6190 forwards to SSH port 22 inside the CVM)
+scp -P 6190 my-model.tar.gz root@localhost:/var/lib/ollama/
+
+# SSH into the CVM and register the model
+ssh -p 6190 root@localhost
+cd /var/lib/ollama && tar -xzvf my-model.tar.gz
+
+cat > /tmp/Modelfile << 'EOF'
+FROM /var/lib/ollama/my-model/weights.gguf
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+SYSTEM "You are a helpful assistant."
+EOF
+
+ollama create my-custom-model -f /tmp/Modelfile
+```
+
+:::note
+For Ubuntu cloud-init CVMs, the default SSH user is `ultraviolet` (password: `password`). For Buildroot CVMs, the default user is `root`.
+:::
+
+#### 2. Verify
+
+```bash
+ollama list
+```
+
+### vLLM
+
+#### 1. Transfer and Configure
+
+vLLM expects a HuggingFace-format model directory:
+
+```bash
+# Copy the model directory into the CVM
+scp -r -P 6190 /path/to/my-hf-model/ root@localhost:/var/lib/vllm/models/
+
+# SSH in and update the vLLM config to point to the new model
+ssh -p 6190 root@localhost
+sed -i 's|^VLLM_MODEL=.*|VLLM_MODEL=/var/lib/vllm/models/my-hf-model|' /etc/vllm/vllm.env
+
+# Restart vLLM to load the new model
+systemctl restart vllm
+# or for SysV init:
+/etc/init.d/S96vllm restart
+```
+
+#### 2. Verify
+
+```bash
+curl http://localhost:8000/v1/models
+```
+
+---
+
+## Verifying Model Availability
+
+After deploying a custom model, verify it is accessible end-to-end through the Cube Agent. From the host:
+
+```bash
+# List available models (port 6193 forwards to the Cube Agent on port 7001 inside the CVM)
+curl http://localhost:6193/v1/models
+
+# Test a chat completion request
+curl http://localhost:6193/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "my-custom-model",
+    "messages": [{"role": "user", "content": "Hello"}]
+  }'
 ```