--- title: "xgboost vs Amphion" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dmlc-xgboost-vs-open-mmlab-amphion" tools: ["dmlc-xgboost", "open-mmlab-amphion"] --- # xgboost vs Amphion *GraphCanon updated Jul 11, 2026* ## Verdict Pick xgboost when xgboost is primarily C++; Amphion is Python; pick Amphion when amphion is primarily Python; xgboost is C++. [xgboost](https://xgboost.readthedocs.io/) reports 29k GitHub stars, 8.9k forks, and 472 open issues, last pushed Jul 10, 2026. [Amphion](https://openhlt.github.io/amphion/) has 9.9k stars, 822 forks, and 175 open issues, last pushed Mar 25, 2026. Figures are from public GitHub metadata via [xgboost's repository](https://github.com/dmlc/xgboost) and [Amphion's repository](https://github.com/open-mmlab/Amphion). | | [xgboost](/tools/dmlc-xgboost.md) | [Amphion](/tools/open-mmlab-amphion.md) | | --- | --- | --- | | Tagline | Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow | Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, | | Stars | 28,553 | 9,927 | | Forks | 8,881 | 822 | | Open issues | 472 | 175 | | Language | C++ | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Computer Vision | Inference & Serving, Speech & Audio, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [xgboost](/tools/dmlc-xgboost.md) | [Amphion](/tools/open-mmlab-amphion.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 107d | | Open issues (now) | 472 | 175 | | Full report | [trust report](/tools/dmlc-xgboost/trust.md) | [trust report](/tools/open-mmlab-amphion/trust.md) | ## Choose when ### Choose xgboost if… - xgboost is primarily C++; Amphion is Python. - License: xgboost is Apache-2.0, Amphion is MIT. - Tags unique to xgboost: gbdt, machine-learning, gbrt, c++. ### Choose Amphion if… - Amphion is primarily Python; xgboost is C++. - License: Amphion is MIT, xgboost is Apache-2.0. - Tags unique to Amphion: audioldm, audio-synthesis, audio-generation, emilia. - Also covers Inference & Serving, Speech & Audio. - Amphion ships Docker support for self-hosted deployment. ## When NOT to use Amphion - Last GitHub push was 108 days ago (slowing maintenance, Mar 25, 2026). Validate activity before betting a new project on Amphion. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between xgboost and Amphion? xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow. Amphion: Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, . See the comparison table for live GitHub stats and shared categories. ### When should I choose xgboost over Amphion? Choose xgboost over Amphion when xgboost is primarily C++; Amphion is Python; License: xgboost is Apache-2.0, Amphion is MIT; Tags unique to xgboost: gbdt, machine-learning, gbrt, c++. ### When should I choose Amphion over xgboost? Choose Amphion over xgboost when Amphion is primarily Python; xgboost is C++; License: Amphion is MIT, xgboost is Apache-2.0; Tags unique to Amphion: audioldm, audio-synthesis, audio-generation, emilia; Also covers Inference & Serving, Speech & Audio; Amphion ships Docker support for self-hosted deployment. ### When should I avoid Amphion? Last GitHub push was 108 days ago (slowing maintenance, Mar 25, 2026). Validate activity before betting a new project on Amphion. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is xgboost or Amphion more popular on GitHub? xgboost has more GitHub stars (28,553 vs 9,927). Stars measure visibility, not whether either tool fits your constraints. ### Are xgboost and Amphion open source? Yes - both are open-source projects on GitHub (xgboost: Apache-2.0, Amphion: MIT). ### Where can I find alternatives to xgboost or Amphion? GraphCanon lists graph-backed alternatives at [xgboost alternatives](/tools/dmlc-xgboost/alternatives) and [Amphion alternatives](/tools/open-mmlab-amphion/alternatives) ([xgboost markdown twin](/tools/dmlc-xgboost/alternatives.md), [Amphion markdown twin](/tools/open-mmlab-amphion/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dmlc-xgboost-vs-open-mmlab-amphion.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, xgboost or Amphion? xgboost: Very active. Amphion: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for xgboost and Amphion? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [xgboost trust report](/tools/dmlc-xgboost/trust); [Amphion trust report](/tools/open-mmlab-amphion/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dmlc-xgboost`](/api/graphcanon/graph?tool=dmlc-xgboost) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_