diff --git a/js/web/lib/wasm/jsep/webgpu/ops/batch-norm.ts b/js/web/lib/wasm/jsep/webgpu/ops/batch-norm.ts
index ec9da2613f406..00a6ca75b34fa 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/batch-norm.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/batch-norm.ts
@@ -108,7 +108,7 @@ const createBatchNormInferenceProgramInfo =
     let inputMean = ${inputMean.getByOffset('cOffset')};
     let inputVar = ${inputVar.getByOffset('cOffset')};
     let x = ${x.getByOffset('global_idx')};
-    let value = (x - inputMean) / sqrt(inputVar + epsilon) * scale + bias;
+    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
     ${y.setByOffset('global_idx', 'value')}
   }`;
       return {
diff --git a/js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts b/js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts
index 056dd54d54591..a835c90bd5451 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts
@@ -92,7 +92,7 @@ const createInstanceNormProgramInfo =
     }
     workgroupBarrier();
 
-    let invStdDev = 1 / sqrt(squaredNormShared / f32(uniforms.normSize) + f32(${attributes.epsilon}));
+    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${attributes.epsilon}));
     let channelScale = invStdDev * f32(${scale.getByOffset('channel')});
     let channelShift = f32(${bias.getByOffset('channel')}) - meanShared * channelScale;
     for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
@@ -212,7 +212,7 @@ const computeMean =
     }
     sum = sum / f32(uniforms.H);
     squaredSum = squaredSum / f32(uniforms.H);
-    let invStdDev = 1 / sqrt(squaredSum - sum * sum + f32(${epsilon}));
+    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${epsilon}));
     let channelScale = invStdDev * ${sumCastType}(scale[currentChannelNumber]);
     let channelShift = ${sumCastType}(bias[currentChannelNumber]) - sum * channelScale;
 
diff --git a/js/web/lib/wasm/jsep/webgpu/ops/layer-norm.ts b/js/web/lib/wasm/jsep/webgpu/ops/layer-norm.ts
index bc446079faf8f..3c9f6ce71bb67 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/layer-norm.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/layer-norm.ts
@@ -93,19 +93,19 @@ const createLayerNormProgramInfo =
       meanSquareVector += value * value;
     }
     let mean = ${sumVector('meanVector', components)} / uniforms.norm_size;
-    let meanSquare = sqrt(${sumVector('meanSquareVector', components)}
-      / uniforms.norm_size - mean * mean + uniforms.epsilon);
+    let invStdDev =
+        inverseSqrt(${sumVector('meanSquareVector', components)} / uniforms.norm_size - mean * mean + uniforms.epsilon);
 
     for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
       let f32input = ${castToF32(dataType, components, 'x[j + offset]')};
       let f32scale = ${castToF32(dataType, components, 'scale[j]')};
-      output[j + offset] = ${variables[0].type.value}((f32input - mean) / meanSquare * f32scale
+      output[j + offset] = ${variables[0].type.value}((f32input - mean) * invStdDev * f32scale
         ${bias ? `+ ${castToF32(dataType, components, 'bias[j]')}` : ''}
       );
     }
 
     ${hasMeanDataOutput ? 'mean_data_output[global_idx] = mean' : ''};
-    ${hasInvStdOutput ? 'inv_std_output[global_idx] = 1 / meanSquare' : ''};
+    ${hasInvStdOutput ? 'inv_std_output[global_idx] = invStdDev' : ''};
   }`;
       };
       const outputs = [{dims: outputShape, dataType: inputs[0].dataType}];
diff --git a/js/web/lib/wasm/jsep/webgpu/ops/skip-layer-norm.ts b/js/web/lib/wasm/jsep/webgpu/ops/skip-layer-norm.ts
index 7e500f865c19b..a2fda9f07d09f 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/skip-layer-norm.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/skip-layer-norm.ts
@@ -132,11 +132,11 @@ const createSkipLayerNormProgramInfo =
           squareSum += f32Value * f32Value;
         }
         let mean = ${sumVector('sum', components)} / hiddenSize;
-        let variance = sqrt(${sumVector('squareSum', components)} / hiddenSize - mean * mean + epsilon);
+        let invStdDev = inverseSqrt(${sumVector('squareSum', components)} / hiddenSize - mean * mean + epsilon);
         ${hasMeanOutput ? 'meanOutput[global_idx] = mean;' : ''}
-        ${hasInvStdDevOutput ? 'invStdOutput[global_idx] = 1.0 / variance;' : ''}
+        ${hasInvStdDevOutput ? 'invStdOutput[global_idx] = invStdDev;' : ''}
         for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {
-          output[offset + i] = (output[offset + i] - ${dataType}(mean)) / ${dataType}(variance) * gamma[i]
+          output[offset + i] = (output[offset + i] - ${dataType}(mean)) * ${dataType}(invStdDev) * gamma[i]
            + ${hasBetaInput ? 'beta[i]' : '0.0'};
         }
       }`;