cudf-cu12

What it is and what it does

cuDF is a GPU-accelerated DataFrame library that mirrors the pandas API, allowing you to work with tabular data on NVIDIA GPUs instead of CPUs. It's part of the RAPIDS suite and includes multiple components: libcudf (the core CUDA C++ library), pylibcudf (Cython bindings), the main cudf library (pandas-like interface), cudf-polars (GPU engine for Polars), and dask-cudf (GPU backend for Dask). The package depends on 18 runtime libraries including CUDA toolkit, cupy, numba, pandas, pyarrow, and GPU-specific NVIDIA libraries.

The primary use case is accelerating data processing workloads by offloading computation to GPU. cuDF offers a drop-in replacement for pandas code through cudf.pandas, which can be invoked with `python -m cudf.pandas` or a Jupyter magic command, requiring no code changes. It also integrates with Polars (via lazy evaluation with `engine="gpu"`) and works as a backend for distributed computing frameworks like Dask and Apache Spark (via Spark RAPIDS).

Use it for:

• Accelerate existing pandas workflows on GPU by running `python -m cudf.pandas script.py` without modifying code.
• Process large parquet or CSV files with GPU-accelerated groupby, filtering, and aggregation operations.
• Build distributed GPU data pipelines using dask-cudf for multi-GPU or multi-node workloads.
• Run Polars queries on GPU by collecting lazy frames with `engine="gpu"` for performance-critical analytics.
• Integrate GPU-accelerated DataFrames into Spark jobs via Spark RAPIDS for hybrid CPU-GPU ETL.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

cuDF is a GPU-accelerated DataFrame library that provides pandas-like APIs for tabular data processing on NVIDIA GPUs, with optional zero-code-change acceleration for existing pandas code via cudf.pandas.

Yes, with conditions. cuDF is actively maintained, permissively licensed, and well-suited for GPU-accelerated tabular data processing. Install only if you have an NVIDIA GPU with compatible CUDA 12 drivers and can manage the 18 runtime dependencies (including CUDA toolkit and GPU libraries). The medium install friction and CUDA version matching requirement are the main barriers; once satisfied, it offers substantial performance gains for data processing workloads that fit GPU memory.

Install

cudf-cu12 on PyPI

pip install cudf-cu12

uv add cudf-cu12

poetry add cudf-cu12

Installing cudf-cu12

Before you install

Medium install friction: requires 18 runtime dependencies including CUDA toolkit, cupy, numba, and GPU-specific libraries (libcudf-cu12, pylibcudf-cu12, rmm-cu12, nvidia-cufile-cu12). Active maintenance with recent release (8 days old) and strong repository signals (9730 stars, last commit 2026-08-14).

License in practice

Apache 2.0 permissive license allows commercial and private use with minimal restrictions, typical for scientific and data processing libraries in the RAPIDS ecosystem.

Quickstart

pip install cudf-cu12

import cudf

df = cudf.read_parquet("data.parquet")
df.dropna().groupby(["A", "B"]).mean()

Requires NVIDIA GPU with compatible CUDA 12 driver and matching CUDA toolkit; CUDA version suffix (-cu12) must match your installed CUDA version.

Verify before relying

• Performance gains over pandas for typical workloads and data sizes not quantified in fact sheet.
• Compatibility matrix with specific GPU models and CUDA minor versions beyond the cu12 designation.
• Memory overhead or limitations compared to pandas DataFrames on GPU.

Package facts

Evidence: cudf_cu12-26.8.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; cudf_cu12-26.8.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

Tags

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