llama.cpp vs SGLang
Short answer
llama.cpp: Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. 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 llama.cpp if you want: no GPU server needed and larger community (133k vs 38.3k GitHub stars).
Choose SGLang if you want: simpler deploy (one container vs a multi-container stack).
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
llama.cpp vs SGLang side by side
| llama.cpp | SGLang | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. | Fast serving engine for LLMs and multimodal models on GPU servers, with an OpenAI-style API. |
| License | MIT | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed. Individual models have their own licenses. | Permissive. Internal and commercial use allowed. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 133k | 38.3k |
| Last commit | Oct 31, 2026 | Oct 31, 2026 |
| Latest release | v0.8.0 | v0.5.22 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | 437 MiB idle, 489 MiB working (our test, vbuild 11804 (c7fc50941), Oct 22, 2026) | Not tested yet |
| 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. | 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 | from €11.99/mo (based on our test, not official) | GPU server (not estimated) |
| Deploy method | Docker (or install script / prebuilt binaries) | Docker (NVIDIA GPU) |
| Known gotchas | 3 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Not documented | Not documented |
| SSO (OIDC / SAML / LDAP) | Not documented | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Not documented | Not documented |
| Details | Full llama.cpp guide → | Full SGLang guide → |
Key differences
- License: llama.cpp: MIT (Permissive). SGLang: Apache-2.0 (Permissive).
- GPU: llama.cpp: optional / see notes. SGLang: needed for practical use.
- Est. server cost: llama.cpp: not estimated (no official RAM figure). SGLang: GPU server (not estimated).
- Deploy: llama.cpp: Docker (or install script / prebuilt binaries) · multi-container stack. SGLang: Docker (NVIDIA GPU) · single container.
- GitHub stars: llama.cpp: 133k. SGLang: 38.3k.
- Latest release: llama.cpp: v0.8.0 (Oct 28, 2026). SGLang: v0.5.22 (Oct 19, 2026).
Same for both: Category: Local model runtimes; Official hardware: not published; Status: Active; Last commit: Oct 31, 2026.
llama.cpp vs SGLang: FAQ
llama.cpp or SGLang: which should a small business choose?
Choose llama.cpp if you want: no GPU server needed and larger community (133k vs 38.3k GitHub stars). Choose SGLang if you want: simpler deploy (one container vs a multi-container stack). llama.cpp publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, llama.cpp or SGLang?
llama.cpp: not estimated (no official RAM figure). 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 llama.cpp and SGLang need?
llama.cpp: not published. SGLang: not published. SGLang needs a GPU for practical use.
What is the license difference between llama.cpp and SGLang?
llama.cpp: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have 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 llama.cpp or SGLang more actively maintained?
llama.cpp: Active, last commit Oct 31, 2026. SGLang: Active, last commit Oct 31, 2026. GitHub data refreshed Oct 31, 2026.
Which is easier to deploy, llama.cpp or SGLang?
llama.cpp installs via Docker (or install script / prebuilt binaries) (multi-container stack); SGLang via Docker (NVIDIA GPU) (single container). llama.cpp lists 3 known gotchas and SGLang lists 4; see each app page.
Related
- llama.cpp self-hosting guide · SGLang self-hosting guide
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- All SGLang alternatives, ranked
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- Ollama vs SGLang
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- SGLang vs vLLM
- Add a third app in the interactive comparison
Sources
- 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
- 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.