diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index e99526ea6..79ff48f44 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -518,7 +518,7 @@ def forward(ctx, A, B, out=None, bias=None, quant_state: Optional[F.QuantState]
         # 1. Dequantize
         # 2. MatmulnN
         print("*******quant_state absmax: ", quant_state.absmax)
-        output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype), bias)
+        output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
 
         # 3. Save state
         ctx.state = quant_state
@@ -549,7 +549,7 @@ def backward(ctx, grad_output):
         # not supported by PyTorch. TODO: create work-around
         # if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
         if req_gradA:
-            grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype))
+            grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
 
         return grad_A, grad_B, None, grad_bias, None
 
diff --git a/bitsandbytes/backends/xpu.py b/bitsandbytes/backends/xpu.py
index 0305964d3..7c8497d48 100644
--- a/bitsandbytes/backends/xpu.py
+++ b/bitsandbytes/backends/xpu.py
@@ -149,6 +149,8 @@ def dequantize_4bit(
         if blocksize is None:
             blocksize = 64
         assert_on_xpu([A, absmax, out])
+        # result = dequantize_4bit_impl(A, quant_state, absmax, out, blocksize, quant_type)
+        # print("+++++++++result: ", result)
         # return dequantize_4bit_impl(A, quant_state, absmax, out, blocksize, quant_type)
         print("------A device: ", A.device)
         print("------quant_state device: ", quant_state.shape[0])
@@ -161,6 +163,8 @@ def dequantize_4bit(
             None,
             blocksize
         )
+        output_dq = output_dq.t()
+        print("=====output_dq: ", output_dq)
         return output_dq        
 
     def gemv_4bit(