diff --git a/docs/FAQ.md b/docs/FAQ.md
index 3875a94b0cb..918d81b40e1 100644
--- a/docs/FAQ.md
+++ b/docs/FAQ.md
@@ -19,7 +19,7 @@ process, we try to stay on top of these changes and release updates as quickly a
 
 The RAPIDS Accelerator for Apache Spark officially supports:
 - [Apache Spark](get-started/getting-started-on-prem.md)
-- [AWS EMR 6.2.0, 6.3.0](get-started/getting-started-aws-emr.md)
+- [AWS EMR 6.2.0, 6.3.0, 6.4.0](get-started/getting-started-aws-emr.md)
 - [Databricks Runtime 7.3, 8.2](get-started/getting-started-databricks.md)
 - [Google Cloud Dataproc 2.0](get-started/getting-started-gcp.md)
 
diff --git a/docs/download.md b/docs/download.md
index 23675ad0289..26248666a76 100644
--- a/docs/download.md
+++ b/docs/download.md
@@ -142,7 +142,7 @@ Software Requirements:
 
 	OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8
 
-	CUDA & Nvidia Drivers*: 11.0 or 11.2 & v450.80.02+
+	CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+
 
 	Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, Cloudera CDP 7.1.7, Databricks 7.3 ML LTS or 8.2 ML Runtime, and GCP Dataproc 2.0
 
@@ -224,13 +224,13 @@ Hardware Requirements:
 
 The plugin is tested on the following architectures: 
 
-	GPU Architecture: NVIDIA V100, T4 and A10/A30/A100 GPUs
+	GPU Architecture: NVIDIA V100, T4 and A2/A10/A30/A100 GPUs
 
 Software Requirements:
 
 	OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8
 	
-	CUDA & Nvidia Drivers*: 11.0 or 11.2 & v450.80.02+
+	CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+
 	
 	Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, Cloudera CDP 7.1.7, Databricks 8.2 ML Runtime, and GCP Dataproc 2.0
 	
diff --git a/docs/get-started/getting-started-on-prem.md b/docs/get-started/getting-started-on-prem.md
index 8d907669a24..f2b069e8d61 100644
--- a/docs/get-started/getting-started-on-prem.md
+++ b/docs/get-started/getting-started-on-prem.md
@@ -52,7 +52,7 @@ Download the RAPIDS Accelerator for Apache Spark plugin jar. Then download the v
 jar that your version of the accelerator depends on. Each cudf jar is for a specific version of
 CUDA and will not run on other versions. The jars use a maven classifier to keep them separate.
 
-- CUDA 11.0/11.1/11.2 => classifier cuda11
+- CUDA 11.x => classifier cuda11
 
 For example, here is a sample version of the jars and cudf with CUDA 11.0 support:
 - cudf-21.10.0-cuda11.jar