diff --git a/docs/Friendly_User/BuildandRun/gpubuildandrun.md b/docs/Friendly_User/BuildandRun/gpubuildandrun.md
index e5034caf7..76276ff11 100644
--- a/docs/Friendly_User/BuildandRun/gpubuildandrun.md
+++ b/docs/Friendly_User/BuildandRun/gpubuildandrun.md
@@ -32,9 +32,9 @@ ml craype-x86-genoa
 This gives us a standard, known programming environment from which to start.  The "source" line give us access to a new set of modules. ml craype-x86-genoa sets optimizations for the cpus on the GPU nodes.
 
 
-After this header we load module specific to the example.  We then compile and run the example.  
+After this header we load modules specific to the example.  We then compile and run the example.  
 
-**If the file "runall" is sourced (after getting an interactive session on 2 gpu nodes) all examples will are run.  This takes about 20 minutes.  You can also sbatch script.**
+**If the file "runall" is sourced (after getting an interactive session on 2 gpu nodes) all examples will run. This takes about 20 minutes. You can also sbatch script.**
 
  
 
diff --git a/docs/Friendly_User/index.md b/docs/Friendly_User/index.md
index 0917fc3cd..9f47d585c 100644
--- a/docs/Friendly_User/index.md
+++ b/docs/Friendly_User/index.md
@@ -14,7 +14,7 @@ An example GPU job allocation command:
 salloc --time=2:00:00 --reservation=<friendly user reservation> --partition=gpu-h100  --account=<project handle>  --nodes=1  -n 1 --mem-per-cpu=8G --gres=gpu:h100:<# of GPUS per node>
 ```
 
-You're automatically given access to all of the memory for the GPU or GPUs requested (80GB per GPU). Note that you'll need to use `-n` to request the number of CPU cores needed, and `--mem` or `--mem-per-cpu` to request the amount of CPU memory needed. You can use `--exclusive` to requst all of the resources on the GPU node.  
+You're automatically given access to all of the memory for the GPU or GPUs requested (80GB per GPU). Note that you'll need to use `-n` to request the number of CPU cores needed, and `--mem` or `--mem-per-cpu` to request the amount of CPU memory needed. You can use `--exclusive` to request all of the resources on the GPU node.  
 
 You can verify whether the GPU device is found by running `nvidia-smi` after landing on the node. If you receive any kind of output other than a `No devices were found` message, there is a GPU waiting to be used by your software.