Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
GPUStack
Unified GPU cluster manager for scalable AI inference
GPUStack orchestrates heterogeneous GPU resources across Linux, macOS, and Windows, delivering OpenAI‑compatible APIs for LLMs, VLMs, diffusion, audio, and embedding models.
Overview
Overview
GPUStack is a lightweight manager that unifies GPUs from NVIDIA, Apple Metal, AMD ROCm, Ascend CANN, and other accelerators into a single inference platform. It supports a broad catalog of models—including large language, vision‑language, diffusion, audio, and embedding models—through flexible backends such as vLLM, llama‑box, Ascend MindIE, and vox‑box. Users interact via a web UI or OpenAI‑compatible endpoints, benefiting from automatic resource evaluation, load balancing, and real‑time GPU monitoring.
Deployment
Deployments are container‑based on Linux (Docker with NVIDIA Container Toolkit) and available as desktop installers for macOS and Windows. Adding nodes or GPUs scales the cluster instantly, while multi‑version backend support lets different models run with their optimal runtimes. API keys and user management secure access, making GPUStack suitable for internal AI services, multi‑tenant SaaS, or research clusters.
Highlights
Broad GPU compatibility across major vendors and OSes
Multi‑version backend support for diverse model runtimes
Distributed inference on heterogeneous multi‑node clusters
OpenAI‑compatible APIs with built‑in user and key management
Pros
- Runs on a wide range of accelerators
- Flexible integration with multiple inference backends
- Scalable architecture for adding GPUs or nodes
- Real‑time GPU performance monitoring
Considerations
- Requires Docker and NVIDIA Container Toolkit for NVIDIA GPUs
- Limited to inference; no native training support
- Multi‑node setup may need additional networking configuration
- Depends on supported accelerators and OS versions
Managed products teams compare with
When teams consider GPUStack, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Enterprises needing on‑prem AI inference at scale
- Teams with heterogeneous GPU hardware across vendors
- Developers wanting OpenAI‑compatible endpoints on private infrastructure
- Researchers requiring multi‑node, multi‑GPU model serving
Not ideal when
- Workflows focused on model training rather than inference
- Environments without Docker or container runtime support
- Small single‑GPU deployments where overhead outweighs benefits
- Organizations preferring fully managed cloud inference services
How teams use it
Internal chatbot powered by LLMs
Deploys Qwen3 or LLaMA models behind OpenAI‑compatible APIs for secure, low‑latency employee assistance.
On‑prem image generation service
Runs Stable Diffusion or FLUX across multiple GPUs, delivering high‑throughput image creation for design teams.
Speech‑to‑text transcription pipeline
Hosts Whisper models, exposing transcription endpoints that scale with added GPU nodes.
Multi‑tenant SaaS inference platform
Provides isolated API keys and load‑balanced inference for diverse customer models on shared GPU clusters.
Tech snapshot
Python92%
Shell2%
PowerShell2%
Jinja2%
Dockerfile1%
Makefile1%
Frequently asked questions
How do I install GPUStack on a Linux server?
Install Docker and the NVIDIA Container Toolkit, then run the provided `docker run` command to start the GPUStack server.
Which GPU accelerators are supported?
GPUStack supports NVIDIA CUDA, Apple Metal, AMD ROCm, Ascend CANN, Hygon DTK, Moore Threads MUSA, Iluvatar Corex, and Cambricon MLU.
Can I add new models not listed in the catalog?
Yes, you can deploy models from Hugging Face, ModelScope, or a local file path by following the UI deployment workflow.
Does GPUStack handle model training?
GPUStack focuses on inference; training workflows are not provided out of the box.
How is API access secured?
API keys are generated per user, displayed only once, and can be managed via the UI's API Keys page.
Project at a glance
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LicenseApache-2.0
Repo age1 year old
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Primary languagePython
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