RAPIDS Accelerator For Apache Spark provides a set of plugins for Apache Spark that leverage GPUs to accelerate Dataframe and SQL processing.

The accelerator is built upon the RAPIDS cuDF project and UCX.

The RAPIDS Accelerator For Apache Spark requires each worker node in the cluster to have CUDA installed.

The RAPIDS Accelerator For Apache Spark consists of two jars: a plugin jar along with the RAPIDS cuDF jar, that is either preinstalled in the Spark classpath on all nodes or submitted with each job that uses the RAPIDS Accelerator For Apache Spark. See the getting-started guide for more details.

Release v25.10.0

Hardware Requirements:

The plugin is designed to work on NVIDIA Volta, Turing, Ampere, Ada Lovelace, Hopper and Blackwell generation datacenter GPUs. The plugin jar is tested on the following GPUs:

GPU Models: NVIDIA V100, T4, A10, A100, L4, H100 and B100 GPUs

Software Requirements: 

OS: Spark RAPIDS is compatible with any Linux distribution with glibc >= 2.28 (Please check ldd --version output).  glibc 2.28 was released August 1, 2018. 
Tested on Ubuntu 22.04, Ubuntu 24.04, Rocky Linux 8 and Rocky Linux 9

NVIDIA Driver*: R525+

Runtime: 
	Scala 2.12, 2.13
	Python, Java Virtual Machine (JVM) compatible with your spark-version. 

	* Check the Spark documentation for Python and Java version compatibility with your specific 
	Spark version. For instance, visit `https://spark.apache.org/docs/3.4.1` for Spark 3.4.1.

Supported Spark versions:
	Apache Spark 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.2.4
	Apache Spark 3.3.0, 3.3.1, 3.3.2, 3.3.3, 3.3.4
	Apache Spark 3.4.0, 3.4.1, 3.4.2, 3.4.3, 3.4.4
	Apache Spark 3.5.0, 3.5.1, 3.5.2, 3.5.3, 3.5.4, 3.5.5, 3.5.6
	Apache Spark 4.0.0, 4.0.1 

Supported Databricks runtime versions for Azure and AWS:
	Databricks 12.2 ML LTS (GPU, Scala 2.12, Spark 3.3.2)
	Databricks 13.3 ML LTS (GPU, Scala 2.12, Spark 3.4.1)
	Databricks 14.3 ML LTS (GPU, Scala 2.12, Spark 3.5.0)

Supported Dataproc versions (Debian/Ubuntu/Rocky):
	GCP Dataproc 2.1
	GCP Dataproc 2.2
	GCP Dataproc 2.3

Supported Dataproc Serverless versions:
	Spark runtime 1.1 LTS
	Spark runtime 1.2
	Spark runtime 2.0
	Spark runtime 2.1
	Spark runtime 2.2

*Some hardware may have a minimum driver version greater than R470. Check the GPU spec sheet for your hardware’s minimum driver version.

*For Cloudera and EMR support, please refer to the Distributions section of the FAQ.

RAPIDS Accelerator’s Support Policy for Apache Spark

The RAPIDS Accelerator maintains support for Apache Spark versions available for download from Apache Spark

Download RAPIDS Accelerator for Apache Spark v25.10.0

Processor Scala Version Download Jar Download Signature Download From Maven
x86_64 Scala 2.12 RAPIDS Accelerator v25.10.0 Signature <pre><dependency>
<groupId>com.nvidia</groupId>
<artifactId>rapids-4-spark_2.12</artifactId>
<version>25.10.0</version>
</dependency></pre>
x86_64 Scala 2.13 RAPIDS Accelerator v25.10.0 Signature <pre><dependency>
<groupId>com.nvidia</groupId>
<artifactId>rapids-4-spark_2.13</artifactId>
<version>25.10.0</version>
</dependency></pre>
arm64 Scala 2.12 RAPIDS Accelerator v25.10.0 Signature <pre><dependency>
<groupId>com.nvidia</groupId>
<artifactId>rapids-4-spark_2.12</artifactId>
<version>25.10.0</version>
<classifier>cuda12-arm64</classifier>
</dependency></pre>
arm64 Scala 2.13 RAPIDS Accelerator v25.10.0 Signature <pre><dependency>
<groupId>com.nvidia</groupId>
<artifactId>rapids-4-spark_2.13</artifactId>
<version>25.10.0</version>
<classifier>cuda12-arm64</classifier>
</dependency></pre>

This package is built against CUDA 12.9. It is tested on V100, T4, A10, A100, L4, H100 and GB100 GPUs with CUDA 12.9.

Verify signature

  • Download the PUB_KEY.
  • Import the public key: gpg --import PUB_KEY
  • Verify the signature for Scala 2.12 jar: gpg --verify rapids-4-spark_2.12-25.10.0.jar.asc rapids-4-spark_2.12-25.10.0.jar
  • Verify the signature for Scala 2.13 jar: gpg --verify rapids-4-spark_2.13-25.10.0.jar.asc rapids-4-spark_2.13-25.10.0.jar

The output of signature verify:

gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <sw-spark@nvidia.com>"

Release Notes

  • Delta Lake liquid clustering read/write/optimize support
  • Delta Lake optimize support
  • Delta Lake deletion vector support with two caveats: need to set useMetadataRowIndex=false and deletion vector support will fall back to the CPU when using the coalescing file reader (these limitations to be removed in a future release)
  • Iceberg insert operations support and improved write job statistics tracking
  • Improved performance for stddev and variance operations in hash based group by aggregations
  • Support for uuid
  • Added Spark 4.0.1 support
  • Added CUDA 13 support, in addition to CUDA 12 support.

Note: There is a known issue in the 25.10.0 release when decompressing gzip files on H100 GPUs. Please find more details in issue-16661.

For a detailed list of changes, please refer to the CHANGELOG.

Archived releases

As new releases come out, previous ones will still be available in archived releases.