diff --git a/unsloth/kernels/utils.py b/unsloth/kernels/utils.py
index 165950a917..0bfd4269b1 100644
--- a/unsloth/kernels/utils.py
+++ b/unsloth/kernels/utils.py
@@ -157,8 +157,8 @@ if HAS_CUDA_STREAM:
             global WEIGHT_BUFFER
             global ABSMAX_BUFFER
             if WEIGHT_BUFFER is None:
-                WEIGHT_BUFFER = torch.empty(size, dtype = dtype, device = "cuda:0")
-                ABSMAX_BUFFER = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0")
+                WEIGHT_BUFFER = torch.empty(size, dtype = dtype, device = "cuda:0", requires_grad = False)
+                ABSMAX_BUFFER = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0", requires_grad = False)
 
             if size > WEIGHT_BUFFER.numel(): WEIGHT_BUFFER.resize_(size)
             if n_elements_absmax > ABSMAX_BUFFER.numel(): ABSMAX_BUFFER.resize_(n_elements_absmax)
@@ -167,11 +167,11 @@ if HAS_CUDA_STREAM:
             out_absmax = ABSMAX_BUFFER[:n_elements_absmax]
         else:
             if out is None:
-                out = torch.empty(shape, dtype = dtype, device = "cuda:0")
+                out = torch.empty(shape, dtype = dtype, device = "cuda:0", requires_grad = False)
             else:
                 assert(out.shape == shape)
                 assert(out.dtype == dtype)
-            out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0")
+            out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0", requires_grad = False)
         pass
 
         # NF4 dequantization of statistics
@@ -224,8 +224,8 @@ else:
             global WEIGHT_BUFFER
             global ABSMAX_BUFFER
             if WEIGHT_BUFFER is None:
-                WEIGHT_BUFFER = torch.empty(size, dtype = dtype, device = "cuda:0")
-                ABSMAX_BUFFER = torch.empty(n_elements_absmax, dtype = dtype, device = "cuda:0")
+                WEIGHT_BUFFER = torch.empty(size, dtype = dtype, device = "cuda:0", requires_grad = False)
+                ABSMAX_BUFFER = torch.empty(n_elements_absmax, dtype = dtype, device = "cuda:0", requires_grad = False)
 
             if size > WEIGHT_BUFFER.numel(): WEIGHT_BUFFER.resize_(size)
             if n_elements_absmax > ABSMAX_BUFFER.numel(): ABSMAX_BUFFER.resize_(n_elements_absmax)
@@ -234,11 +234,11 @@ else:
             out_absmax = ABSMAX_BUFFER[:n_elements_absmax]
         else:
             if out is None:
-                out = torch.empty(shape, dtype = dtype, device = "cuda:0")
+                out = torch.empty(shape, dtype = dtype, device = "cuda:0", requires_grad = False)
             else:
                 assert(out.shape == shape)
                 assert(out.dtype == dtype)
-            out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0")
+            out_absmax = torch.empty(n_elements_absmax, dtype = torch.float32, device = "cuda:0", requires_grad = False)
         pass
 
         # Do dequantization