head to head · open source

llama.cpp vs ServerlessLLM

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. ServerlessLLM has 718 stars, 80 forks, 45 open issues and last shipped 2 months ago. llama.cpp leads on adoption by 18,449% (133,181 vs 718 stars). llama.cpp is written in C++ under MIT; ServerlessLLM is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, ServerlessLLM 11%. llama.cpp was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama.cpp ★ 133K ServerlessLLM ★ 718 category AI & Machine Learning

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Side by side

	llama.cpp	ServerlessLLM
GitHub stars	★ 133K	★ 718
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-09-04
Forks	⑂ 25K	⑂ 80
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

pick ServerlessLLM if

You want the ServerlessLLM feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Python
You evaluated both and ServerlessLLM fits your workflow better

full ServerlessLLM profile →

About llama.cpp

llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp overview →

About ServerlessLLM

ServerlessLLM Load models 10x faster. Serve 10 models with 1 GPU. Docs • Quick Start • OSDI'24 Paper ⚡ Performance ServerlessLLM loads models 6 10x faster than SafeTensors , enabling true serverless deployment where multiple models efficiently share GPU resources. Model Scenario SafeTensors ServerlessLLM Speedup Qwen/Qwen3 32B Random 20.6s 3.2s 6.40x Cached 12.5s 1.3s 9.95x DeepSeek R1 Distill Qwen 32B Random 19.1s 3.2s 5.93x Cached 10.2s 1.2s 8.58x Llama 3.1 8B Instruct Random 4.4s 0.7s 6.54x Results obtained on NVIDIA H100 GPUs with NVMe SSD. "Random" simulates serverless multi model serving; "Cached" shows…

read the full ServerlessLLM overview →

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Frequently asked questions

Is llama.cpp or ServerlessLLM more popular?

llama.cpp has 133,181 GitHub stars and ServerlessLLM has 718. llama.cpp has the larger community by that measure.

Are llama.cpp and ServerlessLLM free?

Both are open source. llama.cpp is licensed under MIT and ServerlessLLM under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and ServerlessLLM?

llama.cpp is written in C++ and ServerlessLLM in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama.cpp or ServerlessLLM?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose ServerlessLLM if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.