diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py
index 265e728862..027dd7b8e9 100644
--- a/unsloth/kernels/fast_lora.py
+++ b/unsloth/kernels/fast_lora.py
@@ -95,12 +95,10 @@ class LoRA_MLP(torch.autograd.Function):
         h = _forward_function(e, g)
         i = matmul_lora(h, downW, downW_quant, downA, downB, downS)
 
-        # custom_fwd disables autocast, so X may arrive in a different dtype than
-        # the fused-op compute dtype (e.g. fp32 hidden states from
-        # fast_rms_layernorm meeting fp16/bf16 base weights). matmul_lora computes
-        # in the base weight dtype (== e.dtype); keep the saved activation in that
-        # same dtype so the backward pass stays dtype-consistent. The incoming
-        # dtype is remembered in ctx.input_dtype and restored on the returned dX.
+        # custom_fwd disables autocast, so X may mismatch the compute dtype (e.g.
+        # fp32 fast_rms_layernorm output meeting fp16/bf16 base weights). Pin the
+        # saved activation to the compute dtype (== e.dtype) so backward stays
+        # consistent; remember the incoming dtype to restore it on the dX grad.
         ctx.input_dtype = dtype
         X = X.to(e.dtype)
 
diff --git a/unsloth/kernels/utils.py b/unsloth/kernels/utils.py
index 89b610f5d0..5bc5ab9a74 100644
--- a/unsloth/kernels/utils.py
+++ b/unsloth/kernels/utils.py
@@ -1092,9 +1092,8 @@ def matmul_lora(
         else:
             W = W.contiguous()
         # custom_fwd disables autocast, so reconcile the activation dtype to the
-        # weight (compute) dtype the way autocast would for a plain Linear. This
-        # covers fp32 hidden states (e.g. fp32 fast_rms_layernorm output) meeting
-        # fp16/bf16 base weights.
+        # weight (compute) dtype as autocast would (e.g. fp32 fast_rms_layernorm
+        # output meeting fp16/bf16 base weights).
         if X.dtype != W.dtype:
             X = X.to(W.dtype)
             dtype = W.dtype