vLLM Documentation

<h2 id="vllm-0271-and-vllm-omni-on-amd-rocm-714">vLLM 0.28.0 and vLLM-Omni on AMD ROCm 7.14</h2>
This 1-Click image ships two serving engines side by side on an AMD ROCm 7.14 host stack running Ubuntu 24.04, preconfigured for AMD Instinct GPUs.
vLLM 0.28.0 is a high-throughput, memory-efficient inference and serving engine for large language models, exposing an OpenAI-compatible API. It delivers state-of-the-art serving throughput with PagedAttention and continuous batching.
vLLM-Omni 0.27.0rc1 extends that engine to omni-modal models, covering text, audio, image and video, and ships its own <code>vllm-omni</code> command line interface. It is built from source against ROCm inside this image and is version-paired with vLLM 0.28.0.
Both engines live inside a single Docker container named <code>rocm</code>, alongside a JupyterLab environment with example notebooks. The login banner prints the JupyterLab URL and token plus ready-to-paste serve commands for both engines.
A note on ROCm versions, which are deliberately different and are not a defect: the host runs ROCm 7.14 for the driver and <code>amd-smi</code>, while the vLLM container carries ROCm 7.2.3, because upstream vLLM does not publish a ROCm 7.14 container image.
This image was validated on a live AMD Instinct MI300X. Verified on that node: a real vLLM completion served over the OpenAI-compatible API, a successful <code>vllm_omni</code> import reporting a ROCm platform, the <code>vllm-omni</code> CLI present, and a real GPU matrix multiplication.

vLLM

vLLM-Omni

ROCm

PyTorch

JupyterLab

Docker

vLLM 0.28.0 for high-throughput LLM serving plus vLLM-Omni 0.27.0rc1 for omni-modal text, audio, image and video inference, on an AMD ROCm 7.14 host, Ubuntu 24.04, ready for AMD Instinct accelerators.

AMD Developer Cloud

vLLM 0.28.0 + vLLM-Omni (ROCm 7.14)

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Getting started after deploying vLLM 0.28.0 + vLLM-Omni (ROCm 7.14)