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SGLang 0.5.14 on AMD ROCm 7.2.4

This 1-Click image ships SGLang 0.5.14, a fast serving framework for large language models and vision language models, running on an AMD ROCm 7.2.4 host stack on Ubuntu 24.04. It is preconfigured for AMD Instinct GPUs and packaged as a ready-to-run Docker container with the SGLang server, alongside a JupyterLab environment with example notebooks.

SGLang delivers high-throughput serving with RadixAttention for prefix caching, continuous batching, and an OpenAI-compatible API.

Software Included

Deploying this Offering using the Control Panel

Click the Deploy to DigitalOcean button to deploy this offering. If you aren’t logged in, this link will prompt you to log in with your DigitalOcean account.

[](https://cloud.digitalocean.com/gpus/new?appId=92df63289931b18c89332046&image=SGLang 0.5.14 (ROCm 7.2.4) 0.5.14 on Ubuntu 24.04&type=applications)

Getting Started After Deploying SGLang 0.5.14 (ROCm 7.2.4)

After the droplet boots, SSH in as root. The Jupyter Lab URL and token, plus the commands to start and inspect the SGLang container, are printed in the login MOTD. SGLang runs inside a Docker container; use the printed docker exec command to open an interactive shell. Verify the GPU stack with amd-smi and rocminfo.