# AMD R9700 Llama.cpp Toolboxes

This project provides pre-built containers (“toolboxes”) for running LLMs on **AMD Radeon AI PRO R9700** GPUs (gfx1201). It uses `toolbox` (standard on Fedora, available on Ubuntu, Arch, etc.) to run `llama.cpp` with full GPU acceleration (Vulkan or ROCm) without messing up your host system.

## Watch the YouTube Video

[![Watch the YouTube Video](https://img.youtube.com/vi/dgyqBUD71lg/maxresdefault.jpg)](https://youtu.be/dgyqBUD71lg) 

## 🚀 Quick Start

### 1. Create a Toolbox
**Which backend to choose?**
*   **Vulkan (RADV)**: Recommended for **stability**. It works reliably with almost all models.
*   **ROCm**: Recommended for **maximum performance**.
    *   *Note*: Multiple ROCm versions are available (e.g., 6.4.4, 7.1, 7.9). Performance can vary significantly depending on the model architecture (e.g., Llama vs. Qwen). **Check the [Benchmarks](https://kyuz0.github.io/amd-r9700-ai-toolboxes/)** to find the best version for your model.

**Option A: Vulkan (RADV) [Recommended]**
```bash
toolbox create llama-vulkan-radv \
  --image docker.io/kyuz0/amd-r9700-toolboxes:vulkan-radv \
  -- --device /dev/dri --group-add video --security-opt seccomp=unconfined
```

**Option B: ROCm (7.2)**
```bash
toolbox create llama-rocm-7.2 \
  --image docker.io/kyuz0/amd-r9700-toolboxes:rocm-7.2 \
  -- --device /dev/dri --device /dev/kfd \
  --group-add video --group-add render --group-add sudo --security-opt seccomp=unconfined
```

> **Ubuntu Users**: `toolbox` may have issues with GPU access. Use [Distrobox](https://github.com/89luca89/distrobox) instead. See [Detailed Guide](#ubuntu-users-distrobox) below.

### 2. Enter the Toolbox
```bash
toolbox enter llama-vulkan-radv
# or: toolbox enter llama-rocm-7.2
```

### 3. Download a Model

**Option A: Manual Download (Recommended)**
Use the `hf` tool to download the model GGUF files to a local directory.

```bash
# Download to models/qwen3-coder-30B-A3B/
HF_HUB_ENABLE_HF_TRANSFER=1 hf download unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF \
  BF16/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
  --local-dir .
```

**Multi-shard Models:**
If a model is split into multiple files (e.g., `00001-of-00005.gguf`), you must download **all** shards to the same folder. The command above ensures all parts are downloaded.

> [!NOTE]
> The old `huggingface-cli` is deprecated. Use the modern `hf` tool (part of `huggingface_hub`).

**Option B: Automatic Download (via llama.cpp)**
`llama.cpp` can automatically download models from the Hugging Face Hub to its internal cache (`~/.cache/huggingface/hub`).

```bash
# Automatically download and run
llama-cli -hf unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF -hf-file BF16/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf ...
```
*Note: We prefer Option A (dedicated folder) to keep things organized, but Option B is great for quick tests.*

### 4. Run a Model

> [!TIP]
> You should **always** use `-fa 1` (Flash Attention). This significantly improves performance and memory utilization on the R9700.

Use **`llama-cli`** for running models directly in your terminal—ideal for quick tests, benchmarking, or chatting without leaving the shell.

Use **`llama-server`** to start an OpenAI-compatible API server. This allows you to connect third-party UIs (like Open WebUI), use the built-in web interface, or build your own applications using standard libraries.

**Run it (CLI Chat):**
```bash
llama-cli -ngl 999 -fa 1 \
  -m models/qwen3-coder-30B-A3B/BF16/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
  -p "Write a R9700 toolkit haiku."
```

**Or run as Server (API + Web UI):**
```bash
llama-server -m models/qwen3-coder-30B-A3B/BF16/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
  -c 8192 -ngl 999 -fa 1
```

---

## 📖 Detailed Guide

### Managing Toolboxes

#### Ubuntu Users (Distrobox)
If you are on Ubuntu, use Distrobox to ensure proper GPU access:
```bash
distrobox create -n llama-rocm-7.2 \
  --image docker.io/kyuz0/amd-r9700-toolboxes:rocm-7.2 \
  --additional-flags "--device /dev/kfd --device /dev/dri --group-add video --group-add render --security-opt seccomp=unconfined"
distrobox enter llama-rocm-7.2
```

#### Updating Toolboxes
To pull the latest images and recreate your toolboxes (useful when Llama.cpp updates):
```bash
# Refresh all toolboxes
./refresh-toolboxes.sh all

# Or refresh specific ones
./refresh-toolboxes.sh llama-vulkan-radv llama-rocm-7.2
```

## 📦 Architecture & Containers

### Backends
*   **Vulkan**: Cross-platform, very stable.
    *   **RADV (Mesa)**: Best compatibility.
    *   **AMDVLK**: Official AMD driver. Faster in some cases but has a strict 2GB single buffer limit (some large models won't load).
*   **ROCm**: AMD's compute stack (CUDA-like).

### Supported Container Images
Images are hosted on [Docker Hub](https://hub.docker.com/r/kyuz0/amd-r9700-toolboxes/tags) and automatically rebuilt on Llama.cpp updates.

| Tag | Backend | Notes |
| :--- | :--- | :--- |
| `vulkan-radv` | Vulkan (Mesa RADV) | Most stable and compatible. Recommended for most users and all models. |
| `vulkan-amdvlk` | Vulkan (AMDVLK) | Fastest backend—AMD open-source driver. ≤2 GiB single buffer allocation limit, some large models won't load. |
| `rocm-6.4.4` | ROCm 6.4.4 (Fedora 43) | Latest stable 6.x build. Uses Fedora 43 packages with backported patch for kernel 6.18.4+ support. |
| `rocm-7.2` | ROCm 7.2 | Latest stable 7.x build. Includes patch for kernel 6.18.4+ support. |
| `rocm7-nightlies` | ROCm 7 Nightlies | Nightly build for ROCm 7. |

## ⚡ Performance & Planning

### Benchmarks
Check the [Interactive Benchmark Viewer](https://kyuz0.github.io/amd-r9700-ai-toolboxes/) or [docs/benchmarks.md](docs/benchmarks.md) to see performance numbers.

### VRAM Estimator
Use the included script to estimate memory usage for models + context. This helps avoid OOM errors.
```bash
gguf-vram-estimator.py models/my-model.gguf --contexts 4096 32768
```
See [docs/vram-estimator.md](docs/vram-estimator.md) for more details.

##  References

*   [Llama.cpp GitHub Repository](https://github.com/ggerganov/llama.cpp)
*   [AMD RDNA™ 4 Architecture](https://www.amd.com/en/products/graphics/rdna-architecture.html)