GPUStack vs llama.cpp
Short answer
GPUStack: GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible API. llama.cpp: Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. Both are in our Local model runtimes category.
Choose GPUStack if you want: simpler deploy (one container vs a multi-container stack).
Choose llama.cpp if you want: no GPU server needed and larger community (132k vs 5.9k GitHub stars).
llama.cpp publishes no official RAM figure, so we cannot compare hosting cost directly.
Last updated · computed from each project's official docs and live GitHub data · sources
GPUStack vs llama.cpp side by side
| GPUStack | llama.cpp | |
|---|---|---|
| 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. | Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. |
| License | Apache-2.0 | MIT |
| 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. Individual models have their own licenses. |
| Status | Active | Active |
| GitHub stars | 5.9k | 132k |
| Last commit | Oct 26, 2026 | Oct 25, 2026 |
| Latest release | v2.3.0 | v0.7.0 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | Not tested yet | 437 MiB idle, 489 MiB working (our test, vbuild 11804 (c7fc50941), Oct 22, 2026) |
| 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. | Optional. Runs on CPU (AVX/AVX2/AVX512/AMX, ARM NEON) and supports CPU+GPU hybrid inference for models larger than VRAM. Docker images exist for CUDA, ROCm, SYCL, Vulkan, MUSA and OpenVINO. |
| Est. server cost | GPU server (not estimated) | from €11.99/mo (based on our test, not official) |
| Deploy method | Docker | Docker (or install script / prebuilt binaries) |
| Known gotchas | 4 listed | 3 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 llama.cpp guide → |
Key differences
- License: GPUStack: Apache-2.0 (Permissive). llama.cpp: MIT (Permissive).
- GPU: GPUStack: needed for practical use. llama.cpp: optional / see notes.
- Est. server cost: GPUStack: GPU server (not estimated). llama.cpp: not estimated (no official RAM figure).
- Deploy: GPUStack: Docker · single container. llama.cpp: Docker (or install script / prebuilt binaries) · multi-container stack.
- GitHub stars: GPUStack: 5.9k. llama.cpp: 132k.
- Last commit: GPUStack: Oct 26, 2026. llama.cpp: Oct 25, 2026.
- Latest release: GPUStack: v2.3.0 (Oct 23, 2026). llama.cpp: v0.7.0 (Oct 15, 2026).
Same for both: Category: Local model runtimes; Official hardware: not published; Status: Active.
GPUStack vs llama.cpp: FAQ
GPUStack or llama.cpp: which should a small business choose?
Choose GPUStack if you want: simpler deploy (one container vs a multi-container stack). Choose llama.cpp if you want: no GPU server needed and larger community (132k vs 5.9k GitHub stars). llama.cpp publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, GPUStack or llama.cpp?
GPUStack: GPU server (not estimated). llama.cpp: not estimated (no official RAM figure). 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 llama.cpp need?
GPUStack: not published. llama.cpp: not published. GPUStack needs a GPU for practical use.
What is the license difference between GPUStack and llama.cpp?
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. llama.cpp: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. This is a summary, not legal advice.
Is GPUStack or llama.cpp more actively maintained?
GPUStack: Active, last commit Oct 26, 2026. llama.cpp: Active, last commit Oct 25, 2026. GitHub data refreshed Oct 26, 2026.
Which is easier to deploy, GPUStack or llama.cpp?
GPUStack installs via Docker (single container); llama.cpp via Docker (or install script / prebuilt binaries) (multi-container stack). GPUStack lists 4 known gotchas and llama.cpp lists 3; see each app page.
Related
- GPUStack self-hosting guide · llama.cpp self-hosting guide
- All GPUStack alternatives, ranked
- All llama.cpp alternatives, ranked
- GPUStack vs LocalAI
- GPUStack vs Ollama
- GPUStack vs vLLM
- GPUStack vs Xinference
- Jan vs llama.cpp
- llama.cpp vs LocalAI
- llama.cpp vs Ollama
- llama.cpp vs vLLM
- llama.cpp 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
- llama.cpp GitHub repository: github.com/ggml-org/llama.cpp
- llama.cpp documentation: github.com/ggml-org/llama.cpp/blob/master/docs/docker.md
- llama.cpp license: github.com/ggml-org/llama.cpp/blob/master/LICENSE
- llama.cpp hardware requirements: github.com/ggml-org/llama.cpp#description
- llama.cpp deploy instructions: github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md#docker
- Hetzner Cloud pricing used for cost estimates (read 2026-10-26): 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.