xgboost-cpu
XGBoost Python Package
Decision gist · record as of 2026-08-14
Yes, if you need XGBoost on x86_64 Linux or Windows and have storage or memory constraints. The active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. No if you require GPU acceleration, federated learning, or run on non-x86_64 platforms—use the standard xgboost package instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Platform support limited to x86_64 Linux and Windows; other platforms require the standard xgboost package.
- Requires Python 3.12 or later.
- Medium install friction due to platform-specific wheels (x86_64 Linux and Windows only); active maintenance with a release 10 days ago and 28654 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 28,654 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 784,597 downloads/mo, #5,071 on PyPI
Alternatives
Verify before relying
pip install xgboost-cpu
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)- Whether performance characteristics differ meaningfully from the standard xgboost package on CPU workloads.
- Compatibility with popular ML frameworks (scikit-learn, pandas pipelines) beyond basic numpy/scipy integration.
- Whether the space savings justify the platform limitation for typical use cases.
What it is and what it does
XGBoost-cpu is a CPU-only variant of the XGBoost gradient boosting library, stripped of GPU acceleration and federated learning support to minimize installation size. It's built for environments where disk or memory constraints make the full XGBoost package impractical, while retaining the core gradient boosting algorithm for classification, regression, and ranking tasks.
The package depends only on numpy and scipy, making it relatively lightweight. It ships as pre-built wheels for x86_64 Linux and Windows platforms only; users on other architectures or seeking GPU support should install the standard xgboost package instead. The library is actively maintained, supports modern Python versions (3.12+), and carries an Apache-2.0 license.
Use it for
- Train gradient boosting models in containerized or resource-limited environments where full XGBoost would exceed storage budgets.
- Build CPU-based ML pipelines on x86_64 Linux or Windows servers without GPU infrastructure.
- Develop and test XGBoost models locally before deploying to GPU-accelerated production systems.
- Use XGBoost in embedded or edge deployments where GPU support is unnecessary or unavailable.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need XGBoost on x86_64 Linux or Windows and have storage or memory constraints.
The active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. No if you require GPU acceleration, federated learning, or run on non-x86_64 platforms—use the standard xgboost package instead.
Install
xgboost-cpu on PyPI
Before you install
Medium install friction due to platform-specific wheels (x86_64 Linux and Windows only); active maintenance with a release 10 days ago and 28654 repository stars. Requires Python 3.12 or later.
Platform support limited to x86_64 Linux and Windows; other platforms require the standard xgboost package. Requires Python 3.12 or later.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install xgboost-cpu
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)Verify before relying
- Whether performance characteristics differ meaningfully from the standard xgboost package on CPU workloads.
- Compatibility with popular ML frameworks (scikit-learn, pandas pipelines) beyond basic numpy/scipy integration.
- Whether the space savings justify the platform limitation for typical use cases.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 784,597 / month, #5,071 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed |
Evidence: xgboost_cpu-3.4.0-py3-none-manylinux_2_28_aarch64.whl; xgboost_cpu-3.4.0-py3-none-manylinux_2_28_x86_64.whl; xgboost_cpu-3.4.0-py3-none-win_amd64.whl; xgboost_cpu-3.4.0-py3-none-win_arm64.whl
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