NVIDIA/cudf

Health v2 · maintenance only

Adoption not part of the score

9734 stars · 1095 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

cuDF is a GPU-accelerated DataFrame library for tabular data processing, part of NVIDIA's RAPIDS suite. It provides a pandas-compatible Python API (cudf), a zero-code-change pandas accelerator (cudf.pandas), a GPU engine for Polars, a Dask backend, and an underlying CUDA C++ core (libcudf) with Apache Arrow-compliant data structures.

Use cases

• accelerate pandas dataframes on gpu
• process large csv datasets faster than pandas
• run pandas code unchanged on gpu with cudf.pandas
• gpu backend for polars queries
• scale dataframe processing with dask on gpus
• etl pipelines for big tabular data
• data analysis of millions of rows without running out of memory

When to choose

• you have nvidia gpus and pandas workloads that are slow or memory-bound
• you want a drop-in pandas accelerator with zero code changes
• you need gpu-accelerated etl or dataframe operations at scale
• you want a gpu engine for polars or dask dataframes

When to avoid

• you have no cuda-capable nvidia gpu
• your datasets are small enough that pandas is already fast enough
• you need cpu-only portability or non-nvidia accelerators
• you rely on pandas features not yet supported by the cudf api

Facets

library · maturity stable

data-science etl gpu-computing database data-science big-data gpu-computing analytics python cpp dataframe pandas rapids cuda arrow dask polars tabular-data data-engineering linux gpu docker

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markdown · JSON · MCP: product_card(name="NVIDIA/cudf")