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GPUStack is an open-source GPU cluster manager that orchestrates inference engines (vLLM, SGLang, TensorRT-LLM) across multi-cloud and on-premises GPU clusters. It provides automated model deployment, load balancing, monitoring, and on-demand SSH-accessible GPU instances for AI workloads.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | gpustack/gpustack |
| Owner | gpustack |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 5.3k |
| Forks | 565 |
| Open issues | 614 |
| Latest release | v2.2.1 (2026-07-03) |
| Last updated | 2026-07-08 |
| Source | https://github.com/gpustack/gpustack |
What gpustack is
Python-based cluster orchestrator supporting NVIDIA, AMD, Ascend, and other accelerators. Manages inference engine lifecycle, handles GPU resource allocation, provides OpenAI-compatible APIs, integrates Prometheus/Grafana for observability, and supports speculative decoding and extended KV cache optimizations. Architecture separates control plane (CPU-only) from distributed worker nodes.
Get the gpustack source
Clone the repository and explore it locally.
git clone https://github.com/gpustack/gpustack.gitcd gpustack# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Docker and NVIDIA Container Toolkit (or equivalent for other accelerators) on all worker nodes; Linux mandatory for workers.
- Server control plane can run on CPU-only node or co-located with a GPU worker; plan network topology for multi-cluster federation.
- Initial setup includes cluster registration, model catalog selection, and API key provisioning; no additional complex configuration for basic deployments.
- Supports broad accelerator ecosystem (NVIDIA, AMD, Ascend, Hygon, MThreads, Iluvatar, MetaX, Cambricon, T-Head) but hardware-specific driver/toolkit setup is a prerequisite.
- Performance tuning available through pre-tuned modes and advanced parameters; baseline performance shown in benchmarks but workload-specific tuning may be required.
When to avoid it — and what to weigh
- Non-Linux Worker Nodes Required — GPUStack worker nodes only support Linux. Windows/macOS are not supported for workers (server can run on Docker Desktop, but this limits scalability).
- Single-Node, Single-GPU Scenarios — If you need a lightweight single-machine solution without multi-cluster orchestration, simpler alternatives (e.g., direct vLLM/SGLang) may have lower operational overhead.
- Proprietary Inference Engine Requirement — GPUStack's pluggable architecture targets standard inference engines. If your workflow requires deeply integrated proprietary inference engines, compatibility must be verified.
- Minimal Operational Overhead Needed — GPUStack introduces cluster management, authentication, monitoring, and metering complexity. Teams wanting zero infrastructure overhead should avoid this.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved open-source license permitting commercial use, modification, and distribution with attribution and liability/warranty disclaimers.
Apache-2.0 clearly permits commercial use, modification, and redistribution. No restrictions on building proprietary products on top. However, any modifications to GPUStack itself must retain Apache-2.0 licensing; consult legal review if bundling or modifying core dependencies. No commercial support, SLA, or indemnification claimed from the open-source project.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Authentication and access control mentioned as enterprise-grade features (API key generation, per-user access). Network isolation between control plane and workers relies on secure token exchange. No audit trail, encryption-at-rest, or network encryption details provided in available data. Requires review: RBAC granularity, audit logging, secrets management, supply-chain security of pulled models and engines. Security posture depends on underlying Docker/Kubernetes/Linux hardening and GPU driver updates.
Alternatives to consider
vLLM (standalone)
Direct inference engine without orchestration; simpler for single or small clusters but lacks multi-cluster federation, authentication, monitoring, and managed model lifecycle.
Ray Serve + vLLM/SGLang
Distributed inference framework with custom serving logic; more flexible but requires more DIY operational work for clustering, model selection, and monitoring compared to GPUStack's built-in features.
Kubernetes + KServe + vLLM
Cloud-native orchestration with standard Kubernetes tooling; good for organizations already invested in K8s but steeper learning curve and requires more manual engine/model configuration than GPUStack's opinionated defaults.
Build on gpustack with DEV.co software developers
Evaluate GPUStack for your AI inference infrastructure. Review the documentation, run the quick-start on a test cluster, and assess multi-cluster federation fit for your environments.
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gpustack FAQ
Can GPUStack run on Windows or macOS?
What GPU types are supported?
Is there commercial support or an SLA?
Can I use proprietary models with GPUStack?
Build it with a software development company
Pairing gpustack with the rest of your stack is where most ai frameworks projects stall. DEV.co is a software development agency that does the integration work — plus AI development when models are in scope.
Ready to Orchestrate Your GPU Clusters?
Evaluate GPUStack for your AI inference infrastructure. Review the documentation, run the quick-start on a test cluster, and assess multi-cluster federation fit for your environments.