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ROCm Serve is a production-grade large language model (LLM) inference server optimized for AMD GPUs. It supports MI300X, MI250X, and RX 7900 series graphics cards, provides OpenAI-compatible API interfaces, and is an ideal alternative to vLLM/llama.cpp workflows.
Section 01
ROCm Serve is a production-grade large language model (LLM) inference server optimized for AMD GPUs. It supports MI300X, MI250X, and RX 7900 series graphics cards, provides OpenAI-compatible API interfaces, and is an ideal alternative to vLLM/llama.cpp workflows.
Section 02
Section 03
In the current field of large language model (LLM) inference services, there is a significant problem: the vast majority of open-source inference frameworks and toolchains are optimized for NVIDIA GPUs by default. This "NVIDIA-first" landscape makes AMD GPU users face challenges such as poor compatibility and difficulty in performance tuning when deploying LLM services. ROCm Serve was born to address this pain point, providing a native, production-grade LLM inference solution for the AMD GPU ecosystem.
Section 04
ROCm Serve is a production-grade LLM inference server designed specifically for AMD GPUs, built on AMD's ROCm (Radeon Open Compute) platform. Positioned as a "plug-and-play" alternative to vLLM and llama.cpp workflows, this project has been deeply optimized for MI300X, MI250X data center GPUs, and RX 7900 series consumer graphics cards.
Section 05
Unlike existing solutions, ROCm Serve adopts an "AMD-first" strategy from the very beginning of its design:
Section 06
ROCm Serve uses a modular design with core components including:
Section 07
| GPU Model | Support Status | Notes |
|---|---|---|
| MI300X | ✅ Full Support | Best performance, supports all data types |
| MI250X | ✅ Full Support | Recommended for multi-GPU configurations |
| MI210 | ✅ Tested | Single GPU workloads |
| RX 7900 XTX | ✅ Tested | Consumer GPU, supports FP16 |
| RX 7800 XT | ⚠️ Experimental | Memory-limited |
Section 08
ROCm Serve is compatible with the HuggingFace transformers ecosystem and supports mainstream open-source models:
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