MI300X Qwen GGUF Inference Setup

MI300X Qwen GGUF Inference Setup | Generated by AI

Home 2026.05

Question: How can I use an AMD MI300X GPU to run inference on a 36GB Qwen model in GGUF format using llama.cpp?

Answer:

Running a large 36 GB Qwen model in GGUF format on an AMD MI300X GPU via llama.cpp involves several steps, because llama.cpp originally targets CPU inference but has experimental support for GPU backends including ROCm (AMD). Here’s a detailed approach:

1. Prepare the Environment

Install ROCm MI300X uses ROCm (AMD’s GPU compute stack). You need a ROCm-compatible Linux system. Install ROCm following the official instructions:
```
sudo apt update
sudo apt install rocm-dkms rocm-dev rocm-utils
```
Verify the GPU is recognized:
```
/opt/rocm/bin/rocminfo
```
Install Dependencies llama.cpp requires CMake and a compiler supporting C++17. Install:
```
sudo apt install build-essential cmake git
```

2. Build `llama.cpp` with ROCm Support

llama.cpp supports AMD GPUs through the ROCm backend (--gpu-roc).

Clone the repo:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

Build with ROCm GPU support:
```
make clean
make ROCM=1
```
You can check the Makefile to ensure ROCM=1 enables HIP/ROCm compilation.

3. Prepare the Qwen GGUF Model

Download the Qwen model in GGUF format (36 GB). Ensure the model is compatible with llama.cpp GGUF support.
Place it in a directory accessible to your inference script.

⚠️ For a 36 GB model, GPU VRAM alone may not suffice if trying fully in-memory. The MI300X has massive HBM (~128 GB total across chips), but check if the software can utilize multiple compute units and memory efficiently.

4. Run Inference with `llama.cpp`

Use the GGUF path with --model:

./main -m /path/to/qwen-36B.gguf -p "Your prompt here" --gpu-roc

Optional flags for performance:
- -c : context size (e.g., 8192 or more)
- -n : number of tokens to generate
- --threads : CPU fallback threads for mixing GPU/CPU
- --memory-f16 or --memory-q4_0 : optimize memory if supported

5. Tips and Notes

Memory Management: For such a large model, consider offloading layers to CPU if GPU memory is exceeded.
Batching: Large models can benefit from batching prompts to optimize throughput.
Debugging: ROCm support in llama.cpp is experimental. Expect some trial and error; logs and verbose mode help.
Alternative: If MI300X is connected to AMD Developer Cloud or MI300 series workstation, ROCm-ready docker images with llama.cpp prebuilt can save setup time.

This approach lets you leverage the MI300X’s high memory bandwidth and ROCm compute for large model inference, while still using a lightweight C++ inference framework like llama.cpp.

References:

If you want, I can draft a ready-to-run shell command template for MI300X to immediately launch inference on your 36 GB Qwen GGUF model with optimal memory settings. It’ll save you trial-and-error time.

Back Donate