04-References
📚 Curated ROCm ResourcesSelected official and community resources for AMD ROCm
Introduction
This section collects high-quality learning resources related to ROCm and AMD GPUs, including official documentation, community tutorials, technical blogs, and relevant news. Use it to quickly find the references you need.
hello-rocm Skill
The hello-rocm Skill is the AI-assistant navigation layer built into this project. It exposes the project’s learning path, reference index, GPU architecture table, deployment tutorials, and troubleshooting checklist to AI coding tools that support Skills, Rules, or Agent configuration.
| If you ask | The Skill indexes |
|---|---|
| Which architecture / gfx target does my GPU use? | docs/en/00-environment/rocm-gpu-architecture-table.md |
| What is the fastest path to run my first model? | src/hello-rocm-skill/references/quick-deploy/SKILL.md |
| How do I install PyTorch / vLLM / Ollama / llama.cpp on ROCm? | The “Frameworks and Inference Services” table on this page |
| How do I debug ROCm / PyTorch / HIP errors? | src/hello-rocm-skill/references/troubleshooting/SKILL.md |
| Which chapter should I read first? | README and chapter index.md files |
Copy-and-use Skill prompt
Copy the sentence below into your AI coding tool and let it decide how to load the Skill through its Skills, Rules, or Agent configuration system:
text
Use src/hello-rocm-skill in the current repository as the hello-rocm Skill. If your tool supports Skills, Rules, or Agent configuration, install or load it in the appropriate place, such as .claude/skills, .cursor/skills, or .agents/skills, then use that Skill to help me learn, deploy, and troubleshoot AMD ROCm.If you prefer manual installation, copy the Skill to the matching directory for your tool:
bash
mkdir -p .claude/skills
cp -r src/hello-rocm-skill .claude/skills/hello-rocmbash
mkdir -p .cursor/skills
cp -r src/hello-rocm-skill .cursor/skills/hello-rocmbash
mkdir -p .agents/skills
cp -r src/hello-rocm-skill .agents/skills/hello-rocmThen start a new conversation and try:
text
Load the hello-rocm skill and help me choose the right ROCm tutorial for my AMD GPU.For troubleshooting and FAQs, you can also join the Feishu community discussion.
Official Resources
AMD Official Documentation
| Resource | Description | Link |
|---|---|---|
| ROCm Documentation | Official ROCm platform docs | rocm.docs.amd.com |
| ROCm Release Notes | Release notes for each version | Release Notes |
| HIP Programming Guide | HIP API and programming guide | HIP Docs |
| AMD GitHub | AMD open-source repositories | github.com/amd |
| ROCm GitHub | ROCm project repositories | github.com/ROCm |
AMD GPU Architecture Whitepapers
| Architecture | Focus | Architecture overview | Whitepaper / Official resource |
|---|---|---|---|
| AMD CDNA Architecture | Instinct MI100 series and Exascale-class GPU compute | AMD CDNA Architecture | AMD CDNA White Paper |
| AMD CDNA 2 Architecture | Instinct MI200 series, scientific computing, and machine learning acceleration | AMD CDNA Architecture | AMD CDNA 2 White Paper |
| AMD CDNA 3 Architecture | Instinct MI300 series for generative AI and HPC acceleration | AMD CDNA Architecture | AMD CDNA 3 White Paper |
| AMD CDNA 4 Architecture | Instinct MI350 series and next-generation AI compute acceleration | AMD CDNA Architecture | AMD CDNA 4 Architecture Whitepaper |
| AMD RDNA Architecture | Radeon graphics and gaming GPUs | AMD RDNA Architecture | AMD RDNA Architecture |
Architecture, Product, and LLVM Target Quick Map
For beginners, start from the product name, identify the architecture, then use the LLVM Target (
gfx) to choose the ROCm / PyTorch installation index. See the “Supported GPU List” below for the full GPU list.
CDNA: Data Center Instinct GPUs
| Architecture | Typical products | LLVM Target | Main use |
|---|---|---|---|
| CDNA 4 | AMD Instinct MI350 series (MI355X, MI350X) | gfx950 | Next-generation AI training / inference and HPC |
| CDNA 3 | AMD Instinct MI300 series (MI325X, MI300X, MI300A) | gfx942 | Generative AI and HPC acceleration |
| CDNA 2 | AMD Instinct MI200 series (MI250X, MI250, MI210) | gfx90a | Scientific computing and machine learning acceleration |
| CDNA | AMD Instinct MI100 series | gfx908 | Exascale-class GPU compute |
RDNA: Radeon GPUs and Ryzen APUs
| Architecture | Typical products / Graphics model | LLVM Target | Main use |
|---|---|---|---|
| RDNA 4 | Radeon RX 9000 series (RX 9070 XT / 9070 GRE / 9070) and Radeon AI PRO R9000 series | gfx1201 | Gaming GPUs, workstation graphics, and AI capabilities |
| RDNA 4 | Radeon RX 9060 XT / 9060 series | gfx1200 | Mainstream gaming GPUs |
| RDNA 3.5 | Ryzen AI Max / Max PRO 300 (Radeon 8060S / 8050S) | gfx1151 | Mobile / APU integrated GPUs |
| RDNA 3.5 | Ryzen AI 300 / AI PRO 400 (Radeon 890M / 880M / 860M) | gfx1150 | Mobile / APU integrated GPUs |
| RDNA 3 | Radeon RX 7900 / PRO W7900 / PRO W7800 series | gfx1100 | High-end consumer and workstation GPUs |
| RDNA 3 | Radeon RX 7800 / 7700 / PRO W7700 / V710 series | gfx1101 | Consumer and workstation GPUs |
| RDNA 3 | Radeon RX 7600 series | gfx1102 | Mainstream consumer GPUs |
| RDNA 3 | Ryzen 200 series (Radeon 780M / 760M / 740M) | gfx1103 | Mobile / APU integrated GPUs |
Frameworks and Inference Services (ROCm Quick Install Links)
This section is designed as a quick lookup index for the hello-rocm Skill: it prioritizes framework or AMD ROCm official installation links, with AMD ROCm Blog searches as practical cross-references for examples and version updates.
| Type | Project | ROCm quick install / official notes | AMD official practice reference | hello-rocm entry |
|---|---|---|---|---|
| Deep learning framework | PyTorch | Install PyTorch for ROCm | AMD ROCm Blog - PyTorch | Environment setup |
| Deep learning framework | TensorFlow | Install TensorFlow for ROCm | AMD ROCm Blog - TensorFlow | Environment setup |
| Deep learning framework | JAX | Install JAX for ROCm | AMD ROCm Blog - JAX | Environment setup |
| Inference service | vLLM | vLLM AMD ROCm installation | AMD ROCm Blog - vLLM | vLLM deployment tutorials |
| Inference service | Ollama | Ollama GPU docs | AMD ROCm Blog - Ollama | Ollama deployment tutorials |
| Inference service | llama.cpp | llama.cpp build docs - HIP/ROCm | AMD ROCm Blog - llama.cpp | llama.cpp deployment tutorials |
| Inference service | LM Studio | LM Studio GPU docs | AMD ROCm Blog - LM Studio | LM Studio deployment tutorials |
| Inference runtime | ONNX Runtime | Install ONNX Runtime for ROCm | AMD ROCm Blog - ONNX Runtime | Environment setup |
Library Documentation
| Library | Purpose | Docs |
|---|---|---|
| rocBLAS | Basic linear algebra | rocBLAS Docs |
| MIOpen | Deep learning primitives | MIOpen Docs |
| RCCL | Collective communication | RCCL Docs |
| rocFFT | Fast Fourier transforms | rocFFT Docs |
| rocSPARSE | Sparse matrix operations | rocSPARSE Docs |
Community Resources
Tutorials & Blogs
- AMD ROCm Blog - Official AMD technical blog
- AMD Developer - AMD developer resource center
- Datawhale - Open-source learning community
Video Tutorials
Coming soon...
Forums & Communities
| Platform | Description | Link |
|---|---|---|
| AMD Community | Official AMD community forum | community.amd.com |
| GitHub Discussions | ROCm project discussions | ROCm Discussions |
| Reddit r/Amd | AMD-related discussions | r/Amd |
News
2026
- 2026.03.11 - ROCm 7.12.0 Preview Release Notes
- Updated ROCm 7.12.0 preview release notes covering ROCm components, installation paths, and platform support changes
- Compatibility information should follow the ROCm 7.12.0 Compatibility Matrix
- pip index URLs are split by GPU architecture, making it easier to choose the matching wheel source in a virtual environment
2025
- 2025.12.11 - ROCm 7.10.0 Released
- Windows platform support
- pip install into Python virtual environments
- TheRock project restructured underlying architecture
More news coming soon...
Hardware Support
Supported GPU List
Instinct Series (Data Center)
| Series | Models | Architecture | LLVM Target | ROCm Support |
|---|---|---|---|---|
| MI350 | MI355X, MI350X | CDNA 4 | gfx950 | ✅ |
| MI300 | MI325X, MI300X, MI300A | CDNA 3 | gfx942 | ✅ |
| MI200 | MI250X, MI250, MI210 | CDNA 2 | gfx90a | ✅ |
| MI100 | MI100 | CDNA | gfx908 | ✅ |
Radeon PRO Series (Workstation)
| Series | Models | Architecture | LLVM Target | ROCm Support |
|---|---|---|---|---|
| AI PRO R9000 | R9700, R9600D | RDNA 4 | gfx1201 | ✅ |
| PRO W7000 | W7900 Dual Slot, W7900, W7800 48GB, W7800 | RDNA 3 | gfx1100 | ✅ |
| PRO W7700 | W7700, V710 | RDNA 3 | gfx1101 | ✅ |
Radeon RX Series (Consumer)
| Series | Models | Architecture | LLVM Target | ROCm Support |
|---|---|---|---|---|
| RX 9000 | RX 9070 XT, 9070 GRE, 9070 | RDNA 4 | gfx1201 | ✅ |
| RX 9000 | RX 9060 XT LP, 9060 XT, 9060 | RDNA 4 | gfx1200 | ✅ |
| RX 7000 | RX 7900 XTX, 7900 XT, 7900 GRE | RDNA 3 | gfx1100 | ✅ |
| RX 7000 | RX 7800 XT, 7700 XT, 7700 XE, 7700 | RDNA 3 | gfx1101 | ✅ |
| RX 7000 | RX 7600 | RDNA 3 | gfx1102 | ✅ |
Ryzen APU Series (Laptop / Mobile)
| Series | Models | Graphics model (iGPU) | Architecture | LLVM Target | ROCm Support |
|---|---|---|---|---|---|
| Ryzen AI Max PRO 300 | AI Max+ PRO 395, Max PRO 390/385/380 | Radeon 8060S | RDNA 3.5 | gfx1151 | ✅ |
| Ryzen AI Max 300 | AI Max+ 395, Max 390, Max 385 | Radeon 8060S / 8050S | RDNA 3.5 | gfx1151 | ✅ |
| Ryzen AI PRO 400 | AI 9 HX PRO 475/470, AI 9 PRO 465, AI 7 PRO 450, AI 5 PRO 440/435 | Radeon 890M / 880M / 860M | RDNA 3.5 | gfx1150 | ✅ |
| Ryzen AI 300 | AI 9 HX 375/370, AI 9 365 | Radeon 890M / 880M | RDNA 3.5 | gfx1150 | ✅ |
| Ryzen 200 | 9 270, 7 260/250, 5 240/230/220, 3 210 | Radeon 780M / 760M / 740M | RDNA 3 | gfx1103 | ✅ |
For the full support list, follow the ROCm 7.12.0 Compatibility Matrix.
Common Tools
Development Tools
| Tool | Purpose | Install Command |
|---|---|---|
| hipcc | HIP compiler | sudo apt install hip-dev |
| rocprof | Performance profiler | sudo apt install rocprofiler |
| rocgdb | GPU debugger | sudo apt install rocgdb |
| hipify-clang | CUDA-to-HIP converter | sudo apt install hipify-clang |
AI Frameworks
| Framework | ROCm Support | Installation |
|---|---|---|
| PyTorch | ✅ | pip install torch --index-url https://download.pytorch.org/whl/rocm6.2 |
| TensorFlow | ✅ | See official docs |
| JAX | ✅ | See official docs |
| ONNX Runtime | ✅ | See official docs |
Recommended Books
Coming soon...
Contributing Resources
If you have quality ROCm-related resources to share, feel free to submit a PR or Issue!
Submission Requirements
- Links must be valid and content must be high-quality
- Provide a short description of the resource
- Organize according to existing categories
Contributions welcome! 🎉