head to head · open source
llama.cpp vs FastDeploy
llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. FastDeploy has 3,724 stars, 758 forks, 649 open issues and last shipped 1 months ago. llama.cpp leads on adoption by 3,476% (133,181 vs 3,724 stars). llama.cpp is written in C++ under MIT; FastDeploy is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, FastDeploy 20%. 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.
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Side by side
| llama.cpp | FastDeploy | |
|---|---|---|
| GitHub stars | ★ 133K | ★ 3.7K |
| License | MIT | Apache-2.0 |
| Written in | C++ | Python |
| Last push | 2026-11-02 | 2026-09-24 |
| Forks | ⑂ 25K | ⑂ 758 |
| 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
pick FastDeploy if
- You want the FastDeploy 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 FastDeploy fits your workflow better
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 FastDeploy
English 简体中文 安装指导 快速入门 支持模型列表 FastDeploy 飞桨大模型高效部署套件 最新活动 [2026 03] FastDeploy v2.5 全新发布! 新增Qwen3 VL与Qwen3 VL MoE模型部署支持，新增W4AFP8量化方法，增强强化学习训练支持能力，包含170+项Bug修复与性能优化，升级全部内容参阅 v2.5 ReleaseNote。 [2026 01] FastDeploy v2.4 : 新增 DeepSeek V3 与 Qwen3 MoE 模型的 PD 分离部署，增强MTP 投机解码能力，全面优化多硬件平台上的 MoE 推理与多模态前缀缓存性能，升级全部内容参阅 v2.4 ReleaseNote。 [2025 11] FastDeploy v2.3 : 新增ERNIE 4.5 VL 28B A3B Thinking与PaddleOCR VL 0.9B两大重磅模型在多硬件平台上的部署支持，进一步优化全方位推理性能，以及带来更多部署功能和易用性的提升，升级全部内容参阅v2.3 ReleaseNote。 [2025 09] FastDeploy v2.2 : HuggingFace生态模型兼容，性能进一步优化，更新增对baidu/ERNIE 21B A3B Thinking支持! [2025 08] FastDeploy v2.1 :全新的KV…
read the full FastDeploy overview →
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Frequently asked questions
Is llama.cpp or FastDeploy more popular?
llama.cpp has 133,181 GitHub stars and FastDeploy has 3,724. llama.cpp has the larger community by that measure.
Are llama.cpp and FastDeploy free?
Both are open source. llama.cpp is licensed under MIT and FastDeploy under Apache-2.0. Neither carries a licence fee.
What is the difference between llama.cpp and FastDeploy?
llama.cpp is written in C++ and FastDeploy 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 FastDeploy?
Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose FastDeploy if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.