diff --git a/docs/source/en/perf_train_gpu_one.mdx b/docs/source/en/perf_train_gpu_one.mdx
index d8c8b3006514e..3cc63afcdc5ee 100644
--- a/docs/source/en/perf_train_gpu_one.mdx
+++ b/docs/source/en/perf_train_gpu_one.mdx
@@ -311,7 +311,7 @@ We can see that this saved some more memory but at the same time training became
 
 ## Floating Data Types
 
-The idea of mixed precision training is that no all variables need to be stored in full (32-bit) floating point precision. If we can reduce the precision the variales and their computations are faster. Here are the commonly used floating point data types choice of which impacts both memory usage and throughput:
+The idea of mixed precision training is that no all variables need to be stored in full (32-bit) floating point precision. If we can reduce the precision the variables and their computations are faster. Here are the commonly used floating point data types choice of which impacts both memory usage and throughput:
 
 - fp32 (`float32`)
 - fp16 (`float16`)