diff --git a/src/aiperf/dataset/dataset_manager.py b/src/aiperf/dataset/dataset_manager.py
index cfa3880a3..749358a87 100644
--- a/src/aiperf/dataset/dataset_manager.py
+++ b/src/aiperf/dataset/dataset_manager.py
@@ -347,8 +347,10 @@ async def _handle_dataset_timing_request(
 
         timing_dataset = []
         for conversation_id, conversation in self.dataset.items():
-            for turn in conversation.turns:
-                timing_dataset.append((turn.timestamp, conversation_id))
+            if conversation.turns:
+                timing_dataset.append(
+                    (conversation.turns[0].timestamp, conversation_id)
+                )
 
         return DatasetTimingResponse(
             service_id=self.service_id,
diff --git a/tests/unit/dataset/test_dataset_manager.py b/tests/unit/dataset/test_dataset_manager.py
index 5ed46e0a6..d0f80f2d5 100644
--- a/tests/unit/dataset/test_dataset_manager.py
+++ b/tests/unit/dataset/test_dataset_manager.py
@@ -220,3 +220,109 @@ async def test_sequential_iterator_wraparound(
 
         finally:
             Path(filename).unlink(missing_ok=True)
+
+    @pytest.mark.asyncio
+    @patch("aiperf.common.tokenizer.Tokenizer.from_pretrained")
+    async def test_dataset_timing_request_for_multi_turn_conversations(
+        self,
+        mock_tokenizer_from_pretrained,
+        create_mooncake_trace_file,
+        mock_tokenizer_cls,
+    ):
+        """Test that dataset timing request returns first turn timestamp for each conversation.
+
+        When a dataset has multiple turns per conversation, the timing dataset should:
+        - Return one entry per conversation (not one per turn)
+        - Use the first turn's timestamp for scheduling
+        - All turns within a conversation are sent sequentially after the conversation is scheduled
+        """
+        # Mock the tokenizer to avoid HTTP requests
+        mock_tokenizer_from_pretrained.return_value = (
+            mock_tokenizer_cls.from_pretrained("test-model")
+        )
+
+        # Create a file with multi-turn conversations
+        entries = [
+            '{"session_id": "sess-1", "timestamp": 0, "input_length": 50, "output_length": 10}',
+            '{"session_id": "sess-1", "delay": 10000, "input_length": 50, "output_length": 10}',
+            '{"session_id": "sess-1", "delay": 10000, "input_length": 100, "output_length": 10}',
+            '{"session_id": "sess-2", "timestamp": 20000, "input_length": 25, "output_length": 20}',
+            '{"session_id": "sess-2", "delay": 10000, "input_length": 10000, "output_length": 20}',
+        ]
+        filename = create_mooncake_trace_file(entries)
+
+        try:
+            user_config = UserConfig(
+                endpoint=EndpointConfig(model_names=["test-model"]),
+                input=InputConfig(
+                    file=filename, custom_dataset_type=CustomDatasetType.MOONCAKE_TRACE
+                ),
+            )
+
+            service_config = ServiceConfig()
+            dataset_manager = DatasetManager(service_config, user_config)
+
+            await dataset_manager.initialize()
+
+            # Configure the dataset to load conversations
+            await dataset_manager._profile_configure_command(
+                ProfileConfigureCommand(config=user_config, service_id="test_service")
+            )
+
+            # Request timing data
+            from aiperf.common.messages import DatasetTimingRequest
+
+            timing_response = await dataset_manager._handle_dataset_timing_request(
+                DatasetTimingRequest(service_id="test_service")
+            )
+
+            # Verify timing dataset structure
+            assert len(timing_response.timing_data) == 2  # 2 conversations, not 5 turns
+
+            # Extract timing data for easier testing
+            timing_dict = {
+                conv_id: timestamp for timestamp, conv_id in timing_response.timing_data
+            }
+
+            # Verify session 1 is scheduled at its first turn's timestamp (0)
+            assert "sess-1" in timing_dict
+            assert timing_dict["sess-1"] == 0
+
+            # Verify session 2 is scheduled at its first turn's timestamp (20000)
+            assert "sess-2" in timing_dict
+            assert timing_dict["sess-2"] == 20000
+
+            # Verify no duplicate session IDs (one per conversation, not per turn)
+            session_ids = [conv_id for _, conv_id in timing_response.timing_data]
+            assert len(session_ids) == len(set(session_ids))
+
+            # Test with conversations containing empty turns (should be skipped)
+            # Manually add a conversation with no turns to dataset
+            from aiperf.common.models import Conversation
+
+            empty_conversation = Conversation(
+                session_id="empty-session",
+                turns=[],  # Empty turns list
+            )
+            dataset_manager.dataset["empty-session"] = empty_conversation
+
+            # Request timing data again
+            timing_response_with_empty = (
+                await dataset_manager._handle_dataset_timing_request(
+                    DatasetTimingRequest(service_id="test_service")
+                )
+            )
+
+            # Verify empty conversation is skipped - should still have 2 entries, not 3
+            assert len(timing_response_with_empty.timing_data) == 2
+            timing_dict_with_empty = {
+                conv_id: timestamp
+                for timestamp, conv_id in timing_response_with_empty.timing_data
+            }
+            # Empty session should not be in timing data
+            assert "empty-session" not in timing_dict_with_empty
+            assert "sess-1" in timing_dict_with_empty
+            assert "sess-2" in timing_dict_with_empty
+
+        finally:
+            Path(filename).unlink(missing_ok=True)