diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index 95a7d9090..91d8e09a4 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -262,7 +262,8 @@ def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
                 B = state.CB
 
         CB = state.CB.data.to(A.dtype).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
-        output = torch.nn.functional.linear(A, CB, bias)
+        with torch.no_grad():
+            output = torch.nn.functional.linear(A, CB, bias)
         ctx.state = state
         ctx.dtype_A = A.dtype
         ctx.grad_shape = A.shape
@@ -317,7 +318,10 @@ def forward(ctx, A, B, out=None, bias=None, quant_state: Optional[F.QuantState]
 
         # 1. Dequantize
         # 2. MatmulnN
-        output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
+        # no_grad prevents F.linear from saving the dequantized weight
+        # (MatMul4Bit.backward re-dequantizes from NF4 anyway)
+        with torch.no_grad():
+            output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
         if out is not None:
             out.copy_(output)
             output = out