diff --git a/comfy/ops.py b/comfy/ops.py
index 98fec1e1d..6ee6075fb 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -167,17 +167,15 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu
             x = to_dequant(x, dtype)
         if not resident and lowvram_fn is not None:
             x = to_dequant(x, dtype if compute_dtype is None else compute_dtype)
-            #FIXME: this is not accurate, we need to be sensitive to the compute dtype
             x = lowvram_fn(x)
-            if (isinstance(orig, QuantizedTensor) and
-                (want_requant and len(fns) == 0 or update_weight)):
+            if (want_requant and len(fns) == 0 or update_weight):
                 seed = comfy.utils.string_to_seed(s.seed_key)
-                y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
-                if want_requant and len(fns) == 0:
-                    #The layer actually wants our freshly saved QT
-                    x = y
-            elif update_weight:
-                y = comfy.float.stochastic_rounding(x, orig.dtype, seed = comfy.utils.string_to_seed(s.seed_key))
+                if isinstance(orig, QuantizedTensor):
+                    y = QuantizedTensor.from_float(x, s.layout_type, scale="recalculate", stochastic_rounding=seed)
+                else:
+                    y = comfy.float.stochastic_rounding(x, orig.dtype, seed=seed)
+            if want_requant and len(fns) == 0:
+                x = y
             if update_weight:
                 orig.copy_(y)
         for f in fns:
@@ -617,7 +615,8 @@ def fp8_linear(self, input):
 
     if input.ndim != 2:
         return None
-    w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+    lora_compute_dtype=comfy.model_management.lora_compute_dtype(input.device)
+    w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True, compute_dtype=lora_compute_dtype, want_requant=True)
     scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
 
     scale_input = torch.ones((), device=input.device, dtype=torch.float32)