Practical overview
When it fits
Use Gpustack when you need a management layer for GPU clusters and model serving, or when users need on-demand SSH-accessible GPU compute rather than only a model endpoint.
Who it is for
- Teams operating GPU resources across on-premises, Kubernetes or cloud environments.
- AI platform teams configuring model-serving engines such as vLLM, SGLang or TensorRT-LLM.
- Developers and researchers who need on-demand GPU instances for development, fine-tuning or interactive workloads.
Documented uses
- Manage GPU capacity across multiple clusters and environments.
- Configure and orchestrate inference engines for AI model serving.
- Provision SSH-accessible GPU instances for hands-on GPU workloads.
Requirements & limits
The project is open source under the Apache-2.0 license. Its documented quick start starts the server with Docker; the README directs operators to retrieve the default administrator password after startup.
- The documented quick start uses Docker and requires a GPU worker with NVIDIA drivers and NVIDIA Container Toolkit.
- A separate CPU-only node can host the GPUStack server; the server can also run on the GPU worker.
- GPUStack lists support for NVIDIA, AMD, Ascend, Hygon, MThreads, Iluvatar, MetaX, Cambricon and T-Head accelerators; requirements vary by accelerator and are covered in installation documentation.
- GPU worker nodes are documented as Linux-only. Windows users are directed to WSL2, and macOS is not supported for worker nodes.
- GPUStack supports vLLM, SGLang and TensorRT-LLM, as well as custom inference engines.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Start the GPUStack server with Docker
Install and start the GPUStack server using the documented Docker command.
Before you start
- Docker installed on the server node.
- A separate CPU-only server node is sufficient; the server can also run on a GPU worker node.
Steps
Run the documented Docker command to start the gpustack/gpustack container, publish port 80, and create the gpustack-data volume.
Check startup output with
sudo docker logs -f gpustack.After startup, use the documented command to retrieve the default administrator password.
Expected outcome
The GPUStack server is started in a Docker container, and the startup instructions provide the next step for obtaining its default administrator password.
Guide limitations
- This guide covers server startup only; it does not include adding a worker or deploying a model.
- GPU worker nodes require Linux. For NVIDIA workers, the project lists an NVIDIA GPU, driver, Docker and NVIDIA Container Toolkit as prerequisites.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Gpustack | Manage GPU capacity across multiple clusters and environments. | Cloud, Docker, Self-hosted, Server | The project is open source under the Apache-2.0 license. Its documented quick start starts the server with Docker; the README directs operators to retrieve the default administrator password after startup. |
| Morphingdb | Import a libtorch model and create predictions from values in a PostgreSQL table. | Docker, Linux, Self-hosted, Server | The README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension. |
| Gateway — pyadav | Send chat-completion requests to a provider through a common server endpoint. | Server | The repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 7 October 2026
- Original website title
- gpustack/gpustack · Official homepage title · retained in its source language