diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index d1b25aa92e8d..ad13c17418b5 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -532,22 +532,37 @@ def fused_moe_kernel(
         a_ptrs += BLOCK_SIZE_K * stride_ak
         b_ptrs += BLOCK_SIZE_K * stride_bk
 
-    # Router weight multiplication MUST happen in float32 before precision
-    # conversion for numerical stability (especially critical on ROCm).
-    if MUL_ROUTED_WEIGHT:
-        moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0)
-        accumulator = accumulator * moe_weight[:, None]
-
+    # Dequantization for supported quantization schemes:
+    #   - int8_w8a16
+    #   - fp8_w8a8
+    #   - int8_w8a8
+    # Accumulator and scalings are in float32 to preserve numerical accuracy.
     if use_int8_w8a16:
         accumulator = accumulator * b_scale
     elif (use_fp8_w8a8 or use_int8_w8a8) and not (group_k > 0 and group_n > 0):
         accumulator = accumulator * a_scale * b_scale
 
-    # Bias is added AFTER dequantization since bias is typically stored in
-    # the output dtype and should not be scaled by quantization factors.
+    # Bias addition:
+    # Bias must be applied after dequantization:
+    #   - Since bias is typically not quantized
+    #   - Bias should not be scaled by quantization factors
     if HAS_BIAS:
-        accumulator = accumulator + bias[None, :]
+        accumulator += bias[None, :]
+
+    # Router (MoE) weight multiplication:
+    # This multiplication MUST be performed in float32 before any precision
+    # conversion to ensure numerical stability, which is especially critical
+    # on ROCm platforms.
+    if MUL_ROUTED_WEIGHT:
+        moe_weight = tl.load(
+            topk_weights_ptr + offs_token,
+            mask=token_mask,
+            other=0,
+        )
+        accumulator *= moe_weight[:, None]
 
+    # Final precision conversion:
+    # Cast once at the end to the desired compute/output dtype.
     accumulator = accumulator.to(compute_type)
 
     # -----------------------------------------------------------