diff --git a/py/torch_tensorrt/dynamo/conversion/impl/shuffle.py b/py/torch_tensorrt/dynamo/conversion/impl/shuffle.py
index 975480f390..ec47126c71 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/shuffle.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/shuffle.py
@@ -25,14 +25,56 @@ def reshape(
     input: TRTTensor,
     shape: Sequence[int],
 ) -> TRTTensor:
+    # Count dynamic dimensions and check for inferred dimension (-1)
+    num_dynamic_dims = 0
+    has_inferred_dim = False
+    inferred_dim_index = -1
+
+    # Create a mutable copy of the shape for modification
+    new_shape = list(shape)
+
+    # Special case: Handle dynamic shape with inferred dimension (-1)
+    # This is required for ops like dynamic_block_quantize_op that requires
+    # dimension to be known at compile time rather than runtime
+    for i, s in enumerate(new_shape):
+        if isinstance(s, TRTTensor):
+            num_dynamic_dims += 1
+        elif s == -1:
+            has_inferred_dim = True
+            inferred_dim_index = i
+
+    # Only process if we have exactly one dynamic dimension and one inferred dimension
+    # This is a common pattern in quantization where one dimension is dynamic
+    # and another needs to be inferred to maintain total element count
+    if has_inferred_dim and num_dynamic_dims == 1:
+        # Calculate the inferred dimension size
+        # Total elements = product of all input dimensions except dynamic shape dim
+        total_elements = 1
+        for s in input.shape:
+            if s != -1:
+                total_elements *= s
+
+        # Divide by known dimensions in new_shape to find the inferred dimension
+        # This ensures the total number of elements remains the same
+        for s in new_shape:
+            if isinstance(s, int) and s != -1:
+                if total_elements % s != 0:
+                    raise ValueError(
+                        f"Cannot infer dimension: {total_elements} elements not divisible by {s}"
+                    )
+                total_elements //= s
+
+        # Replace -1 with the calculated inferred dimension
+        new_shape[inferred_dim_index] = total_elements
+
     layer = ctx.net.add_shuffle(input)
-    if all(isinstance(s, int) for s in shape):
-        layer.reshape_dims = tuple(shape)
+    if all(isinstance(s, int) for s in new_shape):
+        layer.reshape_dims = tuple(new_shape)
     else:
         # Convert all the dimensions to trt Tensors.
         trt_shape = []
 
-        for i, s in enumerate(shape):
+        for i, s in enumerate(new_shape):
             if isinstance(s, TRTTensor):
                 dim_int32 = cast_trt_tensor(
                     ctx,