GPUStack vs SGLang
Short answer
GPUStack: GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible API. SGLang: Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. Both are in our Local model runtimes category.
Choose SGLang if you want: larger community (38.3k vs 5.9k GitHub stars).
Last updated · computed from each project's official docs and live GitHub data · sources
GPUStack vs SGLang side by side
| GPUStack | SGLang | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible API. | Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. |
| License | Apache-2.0 | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. GPUStack Enterprise is a separate commercial edition. Models keep their own licenses. | Permissive. Internal and commercial use allowed. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 5.9k | 38.3k |
| Last commit | Oct 30, 2026 | Oct 31, 2026 |
| Latest release | v2.3.0 | v0.5.22 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | Not tested yet | Not tested yet |
| GPU | Required on worker nodes (Linux only): for NVIDIA, a driver supporting CUDA 12.8 or higher plus the NVIDIA Container Toolkit; for AMD, ROCm 7.0 or higher. The GPUStack server itself does not need a GPU. | Yes for the documented install path: Linux with a supported NVIDIA GPU and a CUDA 13-compatible driver. The project also documents AMD Instinct, Google TPU, Intel GPU and Xeon CPU, Ascend NPU and other platforms on separate hardware pages. |
| Est. server cost | GPU server (not estimated) | GPU server (not estimated) |
| Deploy method | Docker | Docker (NVIDIA GPU) |
| Known gotchas | 4 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Yes, by default | Not documented |
| SSO (OIDC / SAML / LDAP) | Free (OIDC free · SAML free) | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Basic free, more in paid plan | Not documented |
| Details | Full GPUStack guide → | Full SGLang guide → |
Key differences
- Deploy: GPUStack: Docker · single container. SGLang: Docker (NVIDIA GPU) · single container.
- GitHub stars: GPUStack: 5.9k. SGLang: 38.3k.
- Last commit: GPUStack: Oct 30, 2026. SGLang: Oct 31, 2026.
- Latest release: GPUStack: v2.3.0 (Oct 23, 2026). SGLang: v0.5.22 (Oct 19, 2026).
Same for both: Category: Local model runtimes; License: Apache-2.0 (Permissive); Official hardware: not published; GPU: needed for practical use; Est. server cost: GPU server (not estimated); Status: Active.
GPUStack vs SGLang: FAQ
GPUStack or SGLang: which should a small business choose?
Choose SGLang if you want: larger community (38.3k vs 5.9k GitHub stars).
Which is cheaper to self-host, GPUStack or SGLang?
GPUStack: GPU server (not estimated). SGLang: GPU server (not estimated). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.
How much hardware do GPUStack and SGLang need?
GPUStack: not published. SGLang: not published. GPUStack and SGLang needs a GPU for practical use.
What is the license difference between GPUStack and SGLang?
GPUStack: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. GPUStack Enterprise is a separate commercial edition. Models keep their own licenses. SGLang: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed. Models keep their own licenses. This is a summary, not legal advice.
Is GPUStack or SGLang more actively maintained?
GPUStack: Active, last commit Oct 30, 2026. SGLang: Active, last commit Oct 31, 2026. GitHub data refreshed Oct 31, 2026.
Which is easier to deploy, GPUStack or SGLang?
GPUStack installs via Docker (single container); SGLang via Docker (NVIDIA GPU) (single container). GPUStack lists 4 known gotchas and SGLang lists 4; see each app page.
Related
- GPUStack self-hosting guide · SGLang self-hosting guide
- All GPUStack alternatives, ranked
- All SGLang alternatives, ranked
- GPUStack vs llama.cpp
- GPUStack vs LocalAI
- GPUStack vs Ollama
- GPUStack vs TextGen (text-generation-webui)
- GPUStack vs vLLM
- GPUStack vs Xinference
- llama.cpp vs SGLang
- LocalAI vs SGLang
- Ollama vs SGLang
- SGLang vs TextGen (text-generation-webui)
- SGLang vs vLLM
- SGLang vs Xinference
- Add a third app in the interactive comparison
Sources
- GPUStack GitHub repository: github.com/gpustack/gpustack
- GPUStack documentation, GPUStack hardware requirements: docs.gpustack.ai/latest/installation/requirements/
- GPUStack license: github.com/gpustack/gpustack/blob/main/LICENSE
- GPUStack deploy instructions: github.com/gpustack/gpustack#readme
- SGLang GitHub repository: github.com/sgl-project/sglang
- SGLang documentation, SGLang deploy instructions: docs.sglang.io/docs/get-started/install
- SGLang license: github.com/sgl-project/sglang/blob/main/LICENSE
- SGLang hardware requirements: github.com/sgl-project/sglang#supported-hardware
- Hetzner Cloud pricing used for cost estimates (read 2026-10-25): www.hetzner.com/cloud/regular-performance
- GitHub REST API (stars, commits, releases): docs.github.com/en/rest
Every value is generated from the same verified data as the app pages. Spotted something wrong? Tell us.