{"slug": "polars-gpu-engine", "title": "Polars GPU Engine", "summary": "NVIDIA's cuDF library now provides GPU-accelerated execution engines for the Polars Lazy API, enabling users to run dataframe operations on GPUs with automatic fallback to CPU when operations are unsupported. The engine, available in Open Beta through the cudf-polars package, delivers significant speedups for datasets exceeding hundreds of gigabytes and supports scaling from single GPU to multi-GPU and multi-node configurations.", "body_md": "# Polars GPU engine[#](#polars-gpu-engine)\n\ncuDF provides GPU-accelerated execution engines for Python users of the Polars Lazy API. The engines support most of the core expressions and data types as well as a growing set of more advanced dataframe manipulations and data file formats. When a GPU engine is selected, Polars converts expressions into an optimized query plan and determines whether the plan is supported on the GPU. If it is not, the execution transparently falls back to the standard Polars engine and runs on the CPU.\n\n## Install[#](#install)\n\nFollow the [RAPIDS installation guide](https://docs.rapids.ai/install) and pick the\n`cudf-polars`\n\npackage for your CUDA and Python versions. For example, with conda:\n\n```\nconda install -c rapidsai -c conda-forge -c nvidia cudf-polars\n```\n\nOr with pip (CUDA 13 wheels; use `cudf-polars-cu12`\n\nfor CUDA 12):\n\n```\npip install cudf-polars-cu13\n```\n\n## Quick start[#](#quick-start)\n\n[ RayEngine](api/#cudf_polars.engine.ray.RayEngine) with no arguments uses\nevery GPU visible to the process, so the same code runs on one GPU and scales to multi-GPU /\nmulti-node setups automatically:\n\n``` python\nimport polars as pl\nfrom cudf_polars.engine.ray import RayEngine\n\nquery = (\n    pl.scan_parquet(\"/data/dataset/*.parquet\")\n    .filter(pl.col(\"amount\") > 100)\n    .group_by(\"customer_id\")\n    .agg(pl.col(\"amount\").sum())\n)\n\nwith RayEngine() as engine:\n    result = query.collect(engine=engine)\n```\n\nSee [Usage](usage/) for the full tutorial, [Engines](engines/) for a conceptual overview of the\navailable engines, [Configuration Options](options/) for the\n[ StreamingOptions](api/#cudf_polars.engine.options.StreamingOptions) configuration, and\n\n[Understanding Memory Use in the GPU Streaming Engine](memory_errors/)for guidance on out-of-memory errors and memory tuning.\n\n## Benchmark[#](#benchmark)\n\nPolars delivers high performance across a wide range of data scales through multiple execution engines. The default CPU engine is highly optimized for interactive and medium-scale analytics on a single node. The Polars GPU engine lets you move seamlessly to GPU nodes, providing meaningful acceleration when your dataset grows to hundreds of gigabytes or more.\n\nWe ran the Polars Decision Support (PDS) benchmarks to compare the Polars GPU engine with the CPU engine at larger scale factors to show how the GPU engine delivers meaningful speedups as dataset size grows:\n\nOn a single GPU, you can run TB-scale workloads with significant speedups compared to running on CPU. You can also scale up to run on multiple GPUs for processing even larger workloads:\n\nFor more information on the benchmarks being run, see the PDS-DS queries in the [cuDF GitHub repository](https://github.com/rapidsai/cudf/tree/release/26.06/python/cudf_polars/cudf_polars/streaming/benchmarks).\n\n## Learn More[#](#learn-more)\n\nThe GPU engine for Polars is now available in Open Beta and the engine is undergoing rapid development.\nTo learn more, visit the [GPU Support page](https://docs.pola.rs/user-guide/gpu-support/) on the Polars website.", "url": "https://wpnews.pro/news/polars-gpu-engine", "canonical_source": "https://docs.rapids.ai/api/cudf/stable/cudf_polars/", "published_at": "2026-06-11 21:40:02+00:00", "updated_at": "2026-06-11 21:49:22.322182+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-tools", "machine-learning"], "entities": ["cuDF", "Polars", "RAPIDS", "CUDA", "RayEngine"], "alternates": {"html": "https://wpnews.pro/news/polars-gpu-engine", "markdown": "https://wpnews.pro/news/polars-gpu-engine.md", "text": "https://wpnews.pro/news/polars-gpu-engine.txt", "jsonld": "https://wpnews.pro/news/polars-gpu-engine.jsonld"}}