diff --git a/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td b/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
index e0533fc184..38f8a5ccc5 100644
--- a/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
+++ b/include/ttmlir/Dialect/TTNN/IR/TTNNOps.td
@@ -316,22 +316,17 @@ def TTNN_EmbeddingOp : TTNN_Op<"embedding"> {
     let hasVerifier = 1;
 }
 
-def TTNN_SoftmaxOp : TTNN_NamedDPSOp<"softmax"> {
+def TTNN_SoftmaxOp : TTNN_Op<"softmax"> {
     let summary = "Softmax op.";
     let description = [{
       Softmax operation.
     }];
 
     let arguments = (ins AnyRankedTensor:$input,
-                         AnyRankedTensor:$output,
                          SI32Attr: $dimension);
 
     let results = (outs AnyRankedTensor:$result);
 
-    let extraClassDeclaration = [{
-      MutableOperandRange getDpsInitsMutable() { return getOutputMutable(); }
-    }];
-
     let hasVerifier = 1;
 }
 
diff --git a/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp b/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
index a895acc182..31f10ab097 100644
--- a/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
+++ b/lib/Conversion/TTIRToTTNN/TTIRToTTNN.cpp
@@ -301,9 +301,10 @@ class SoftmaxOpConversionPattern : public OpConversionPattern<ttir::SoftmaxOp> {
   LogicalResult
   matchAndRewrite(ttir::SoftmaxOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
+    removeDpsOp(rewriter, adaptor);
     rewriter.replaceOpWithNewOp<ttnn::SoftmaxOp>(
         op, this->getTypeConverter()->convertType(op.getType()),
-        adaptor.getInput(), adaptor.getOutput(), adaptor.getDimension());
+        adaptor.getInput(), adaptor.getDimension());
     return success();
   }
 };
diff --git a/lib/Dialect/TTNN/IR/TTNNOps.cpp b/lib/Dialect/TTNN/IR/TTNNOps.cpp
index 78650dff05..506a45f6d3 100644
--- a/lib/Dialect/TTNN/IR/TTNNOps.cpp
+++ b/lib/Dialect/TTNN/IR/TTNNOps.cpp
@@ -107,7 +107,7 @@ ::mlir::LogicalResult mlir::tt::ttnn::EmbeddingOp::verify() {
 
 ::mlir::LogicalResult mlir::tt::ttnn::SoftmaxOp::verify() {
   ::mlir::RankedTensorType inputType = getInput().getType();
-  ::mlir::RankedTensorType outputType = getOutput().getType();
+  ::mlir::RankedTensorType outputType = getResult().getType();
 
   // Shapes of input and output of a softmax operation must be the same
   if (inputType.getShape() != outputType.getShape()) {
diff --git a/lib/Target/TTNN/TTNNToFlatbuffer.cpp b/lib/Target/TTNN/TTNNToFlatbuffer.cpp
index cfed0120fa..78d0dff2ac 100644
--- a/lib/Target/TTNN/TTNNToFlatbuffer.cpp
+++ b/lib/Target/TTNN/TTNNToFlatbuffer.cpp
@@ -390,8 +390,8 @@ ::flatbuffers::Offset<::tt::target::ttnn::SoftmaxOp>
 createSoftmaxOp(FlatbufferObjectCache &cache, SoftmaxOp op) {
   auto in =
       cache.at<::tt::target::TensorRef>(getOperandThroughDPSOps(op.getInput()));
-  auto out = cache.at<::tt::target::TensorRef>(
-      getOperandThroughDPSOps(op.getResult()));
+  auto out = cache.getOrCreate(op.getResult(), tensorValueToFlatbuffer,
+                               kHostAllocatedAddress, kHostAllocatedSize);
   int32_t dimension = op.getDimension();
 
   return ::tt::target::ttnn::CreateSoftmaxOp(*cache.fbb, in, out, dimension);
diff --git a/test/ttmlir/Dialect/TTNN/softmax/simple_softmax.mlir b/test/ttmlir/Dialect/TTNN/softmax/simple_softmax.mlir
index f9a0a8fff0..ec05a3006e 100644
--- a/test/ttmlir/Dialect/TTNN/softmax/simple_softmax.mlir
+++ b/test/ttmlir/Dialect/TTNN/softmax/simple_softmax.mlir
@@ -2,12 +2,10 @@
 #any_device = #tt.operand_constraint<dram|l1|tile|any_device|any_device_tile>
 module attributes {} {
   func.func @forward(%arg0: tensor<512x1024xbf16>) -> tensor<512x1024xbf16> {
-    // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
     %0 = tensor.empty() : tensor<512x1024xbf16>
     // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
     // Check for positive dimension attribute
     %1 = "ttir.softmax"(%arg0, %0) <{dimension = 1 : si32, operand_constraints = [#any_device, #any_device]}> : (tensor<512x1024xbf16>, tensor<512x1024xbf16>) -> tensor<512x1024xbf16>
-    // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
     %2 = tensor.empty() : tensor<512x1024xbf16>
     // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
     // Check for negative dimension attribute
diff --git a/test/ttmlir/Silicon/TTNN/sharded/simple_eltwise_sharded.mlir b/test/ttmlir/Silicon/TTNN/sharded/simple_eltwise_sharded.mlir
index b398d070fa..902f3aa598 100644
--- a/test/ttmlir/Silicon/TTNN/sharded/simple_eltwise_sharded.mlir
+++ b/test/ttmlir/Silicon/TTNN/sharded/simple_eltwise_sharded.mlir
@@ -83,12 +83,10 @@ func.func @sqrt(%arg0: tensor<224x64xf32>) -> tensor<224x64xf32> {
 }
 
 func.func @softmax(%arg0: tensor<224x64xbf16>) -> tensor<224x64xbf16> {
-  // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
   %0 = tensor.empty() : tensor<224x64xbf16>
   // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
   // Check for positive dimension attribute
   %1 = "ttir.softmax"(%arg0, %0) <{dimension = 1 : si32, operand_constraints = [#l1_block_sharded, #l1_block_sharded]}> : (tensor<224x64xbf16>, tensor<224x64xbf16>) -> tensor<224x64xbf16>
-  // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
   %2 = tensor.empty() : tensor<224x64xbf16>
   // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
   // Check for negative dimension attribute
diff --git a/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir b/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
index 5977e46b7b..3d5cbba155 100644
--- a/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
+++ b/test/ttmlir/Silicon/TTNN/simple_eltwise.mlir
@@ -106,12 +106,10 @@ func.func @rsqrt(%arg0: tensor<64x128xf32>) -> tensor<64x128xf32> {
 }
 
 func.func @softmax(%arg0: tensor<512x1024xbf16>) -> tensor<512x1024xbf16> {
-  // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
   %0 = tensor.empty() : tensor<512x1024xbf16>
   // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
   // Check for positive dimension attribute
   %1 = "ttir.softmax"(%arg0, %0) <{dimension = 1 : si32, operand_constraints = [#any_device, #any_device]}> : (tensor<512x1024xbf16>, tensor<512x1024xbf16>) -> tensor<512x1024xbf16>
-  // CHECK: %[[C:.*]] = "ttnn.empty"[[C:.*]]
   %2 = tensor.empty() : tensor<512x1024xbf16>
   // CHECK: %[[C:.*]] = "ttnn.softmax"[[C:.*]]
   // Check for negative dimension attribute