# llamacpp-rocm
**Repository Path**: Snow_Nee/llamacpp-rocm
## Basic Information
- **Project Name**: llamacpp-rocm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-05-25
- **Last Updated**: 2026-05-25
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# llamacpp-rocm
)
We provide nightly builds of **llama.cpp** with **AMD ROCm™ 7** acceleration based on TheRock - delivering the freshest, cutting-edge builds available. Our automated pipeline specifically targets seamless integration with [**🍋 Lemonade**](https://github.com/lemonade-sdk/lemonade) and similar AI applications requiring high-performance GPU inference.
> [!IMPORTANT]
> **Contribution & Support Notice**: While this project currently focuses on integrating llama.cpp+ROCm in a specific production context, our broader goal is to contribute meaningfully to the llama.cpp+ROCm ecosystem. We're not set up to provide comprehensive technical support, but we welcome collaborations, idea exchanges, or contributions that help advance this space.
## 🎯 Supported Devices
This build specifically targets the following GPU architectures:
- **gfx1151** (STX Halo APU) - Ryzen AI MAX+ Pro 395
- **gfx1150** (STX Point APU) - Ryzen AI 300
- **gfx120X** (RDNA4 GPUs) - includes AMD Radeon RX 9070 XT/GRE/9070, RX 9060 XT/9060
- **gfx110X** (RDNA3 GPUs) - includes AMD Radeon dGPUs: PRO W7900/W7800/W7700/W7600, RX 7900 XTX/XT/GRE, RX 7800 XT, RX 7700 XT/7700, RX 7600 XT/7600 and iGPUs: Radeon 780M/760M/740M
- **gfx103X** (RDNA2 GPUs) - includes AMD Radeon dGPUs: RX 6800 XT/6800, RX 6700 XT/6700, RX 6600 XT/6600, RX 6500 XT/6500
**All builds include ROCm™ 7 built-in** - no separate ROCm™ installation required!
## 🚀 Automated Builds
Our automated GitHub Actions workflow creates nightly builds for:
- **Windows** and **Ubuntu** operating systems
- **Multiple GPU targets**: `gfx1151`, `gfx1150`, `gfx110X`, `gfx120X`, `gfx103X`
- **ROCm™ 7 built-in** - complete runtime libraries included
| GPU Target | Ubuntu | Windows |
|-------------|--------|---------|
| **gfx110X** | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) |
| **gfx1150** | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) |
| **gfx1151** | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) |
| **gfx120X** | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) |
| **gfx103X** | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) | [](https://github.com/aigdat/llamacpp-rocm/releases/latest) |
> **⚡ Ready to Run**: All releases include complete ROCm™ 7 runtime libraries - just download and go!
> **Linux (gfx1150/APU):** OOM despite free VRAM? Add `ttm.pages_limit=12582912` (48 GB) to the kernel cmdline (e.g. GRUB), run `update-grub`, then reboot. See [TheRock FAQ](https://github.com/ROCm/TheRock/blob/main/docs/faq.md#gfx1151-strix-halo-specific-questions) for more.
---
## 🧪 Quick Smoketest
To verify your download is working correctly:
1. **Download** the appropriate build for your GPU target from our [latest releases](https://github.com/aigdat/llamacpp-rocm/releases/latest)
2. **Extract** the archive to your preferred directory
3. **Test** with any GGUF model from Hugging Face:
```bash
llama-server -m YOUR_GGUF_MODEL_PATH -ngl 99
```
> **đź’ˇ Tip**: Use `-ngl 99` to offload all layers to GPU for maximum acceleration. The exact number of layers may vary by model, but 99 ensures all available layers are offloaded.
> **🍋 Lemonade Integration**: You can also test these builds directly with [**Lemonade**](https://github.com/lemonade-sdk/lemonade) for a seamless AI application experience *(coming soon!)*
---
## 📦 Dependencies
This project relies on the following external software and tools:
### Core Dependencies
- **[Llama.cpp](https://github.com/ggerganov/llama.cpp)** - Efficient, cross-platform inference engine for running GGUF models locally.
- **[ROCm SDK (TheRock)](https://github.com/ROCm/TheRock)** - AMD’s open-source platform for GPU-accelerated computing.
- **[HIP](https://github.com/ROCm/HIP)** - C++ API for writing portable GPU code within the ROCm ecosystem.
### Build Tools & Compilers
- **[Visual Studio 2022 Build Tools](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2022)** - Microsoft C++ build tools
- **[CMake](https://cmake.org/)** - Cross-platform build system (version 3.31.0)
- **[Ninja](https://ninja-build.org/)** - Small build system with focus on speed
- **[Clang/Clang++](https://clang.llvm.org/)** - C/C++ compiler (bundled with ROCm)
---
## 🏗️ Code and Artifact Structure
> [!NOTE]
> **Active Development**: This project is under active development. Code and artifact structure are subject to change as we continue to improve and expand functionality.
### Key Components
- **`docs/`** - Contains build documentation and setup guides
- **`utils/`** - Houses utility scripts for build automation and dependency management
- **GitHub Actions Workflows** - Located in `.github/workflows/` (automated build pipeline)
- **Build Artifacts** - Generated during CI/CD and published as releases
The build process is primarily handled through GitHub Actions, with the repository serving as the source for automated compilation and packaging of llama.cpp with ROCm™ 7 support.
---
## đź“‹ Manual Build Instructions
For detailed manual build instructions, please see: **[docs/manual_instructions.md](docs/manual_instructions.md)**
## đź“„ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.