diff --git a/fastdeploy/engine/common_engine.py b/fastdeploy/engine/common_engine.py
index 4850fdfe06..ad2f6bf845 100644
--- a/fastdeploy/engine/common_engine.py
+++ b/fastdeploy/engine/common_engine.py
@@ -49,6 +49,7 @@ from fastdeploy.engine.request import (
 )
 from fastdeploy.engine.resource_manager import ResourceManager
 from fastdeploy.engine.sched.resource_manager_v1 import ResourceManagerV1
+from fastdeploy.engine.sched.scheduler_metrics_logger import SchedulerMetricsLogger
 from fastdeploy.eplb.utils import init_eplb_signals
 from fastdeploy.input.preprocess import InputPreprocessor
 from fastdeploy.inter_communicator import (
@@ -146,6 +147,13 @@ class EngineService:
         )
         self.token_processor.set_resource_manager(self.resource_manager)
 
+        self.scheduler_metrics_logger = SchedulerMetricsLogger(
+            enabled=True,
+            dp_rank=self.cfg.parallel_config.local_data_parallel_id,
+        )
+        self.resource_manager.scheduler_metrics_logger = self.scheduler_metrics_logger
+        self.token_processor.set_scheduler_metrics_logger(self.scheduler_metrics_logger)
+
         self.partial_chunked_tokens = [0] * (self.cfg.max_num_partial_prefills + 1)
         for idx in range(1, self.cfg.max_num_partial_prefills + 1):
             self.partial_chunked_tokens[idx] = (
diff --git a/fastdeploy/engine/sched/resource_manager_v1.py b/fastdeploy/engine/sched/resource_manager_v1.py
index d7bb878eb4..5ad6854f4d 100644
--- a/fastdeploy/engine/sched/resource_manager_v1.py
+++ b/fastdeploy/engine/sched/resource_manager_v1.py
@@ -957,6 +957,36 @@ class ResourceManagerV1(ResourceManager):
                 if self.current_reserve_output_block_num == 0:
                     self.can_relax_prefill_strategy = True
 
+            if hasattr(self, "scheduler_metrics_logger") and self.scheduler_metrics_logger is not None:
+                total_blocks = self.total_block_number()
+                free_blocks = self.available_block_num()
+                used_blocks = max(total_blocks - free_blocks, 0)
+                tokens_used = used_blocks * self.config.cache_config.block_size
+                token_usage = used_blocks / total_blocks if total_blocks > 0 else 0.0
+                running_cnt = len(self.running)
+                queue_cnt = len(self.waiting)
+
+                prefill_reqs = [
+                    r for r in scheduled_reqs if isinstance(r, Request) and r.task_type == RequestType.PREFILL
+                ]
+                has_decode = any(getattr(r, "task_type", None) == RequestType.DECODE for r in scheduled_reqs)
+
+                self.scheduler_metrics_logger.log_prefill_batch(
+                    prefill_reqs=prefill_reqs,
+                    running_cnt=running_cnt,
+                    queue_cnt=queue_cnt,
+                    tokens_used=tokens_used,
+                    token_usage=token_usage,
+                )
+                if has_decode:
+                    self.scheduler_metrics_logger.log_decode_batch(
+                        running_cnt=running_cnt,
+                        queue_cnt=queue_cnt,
+                        tokens_used=tokens_used,
+                        token_usage=token_usage,
+                        use_cudagraph=self.config.graph_opt_config.use_cudagraph,
+                    )
+
             self.update_metrics()
 
             return scheduled_reqs, error_reqs
diff --git a/fastdeploy/engine/sched/scheduler_metrics_logger.py b/fastdeploy/engine/sched/scheduler_metrics_logger.py
new file mode 100644
index 0000000000..c896e6bf16
--- /dev/null
+++ b/fastdeploy/engine/sched/scheduler_metrics_logger.py
@@ -0,0 +1,124 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import logging
+import threading
+import time
+from typing import Iterable
+
+
+class SchedulerMetricsLogger:
+    """
+    Lightweight console logger for scheduler-level prefill/decode metrics.
+    """
+
+    def __init__(self, enabled: bool = True, dp_rank: int = 0) -> None:
+        self.enabled = enabled
+        self.dp_rank = dp_rank
+        self._lock = threading.Lock()
+        self._last_decode_tic = time.perf_counter()
+        self._decode_tokens_since_last = 0
+        self._logger = self._get_logger()
+
+    def _get_logger(self) -> logging.Logger:
+        logger = logging.getLogger("fastdeploy.scheduler_metrics")
+        if not getattr(logger, "_fd_scheduler_metrics_configured", False):
+            logger.setLevel(logging.INFO)
+            logger.propagate = False
+            handler = logging.StreamHandler()
+            formatter = logging.Formatter(
+                "[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s",
+                "%Y-%m-%d %H:%M:%S",
+            )
+            handler.setFormatter(formatter)
+            logger.addHandler(handler)
+            logger._fd_scheduler_metrics_configured = True
+        return logger
+
+    def on_decode_tokens(self, num_tokens: int) -> None:
+        if not self.enabled:
+            return
+        if num_tokens <= 0:
+            return
+        with self._lock:
+            self._decode_tokens_since_last += num_tokens
+
+    def log_prefill_batch(
+        self,
+        prefill_reqs: Iterable,
+        running_cnt: int,
+        queue_cnt: int,
+        tokens_used: int,
+        token_usage: float,
+    ) -> None:
+        if not self.enabled:
+            return
+        prefill_reqs = list(prefill_reqs)
+        if not prefill_reqs:
+            return
+
+        new_tokens = 0
+        cached_tokens = 0
+        for req in prefill_reqs:
+            start = getattr(req, "prefill_start_index", 0) or 0
+            end = getattr(req, "prefill_end_index", 0) or 0
+            if end > start:
+                new_tokens += end - start
+            cached_tokens += getattr(req, "num_cached_tokens", 0) or 0
+
+        msg = (
+            "Prefill batch, "
+            f"dp_rank: {self.dp_rank}, "
+            f"#new-seq: {len(prefill_reqs)}, "
+            f"#new-token: {new_tokens}, "
+            f"#cached-token: {cached_tokens}, "
+            f"token usage: {token_usage:.2f}, "
+            f"#running-req: {running_cnt}, "
+            f"#queue-req: {queue_cnt}, "
+        )
+        self._logger.info(msg)
+
+    def log_decode_batch(
+        self,
+        running_cnt: int,
+        queue_cnt: int,
+        tokens_used: int,
+        token_usage: float,
+        use_cudagraph: bool,
+    ) -> None:
+        if not self.enabled:
+            return
+        with self._lock:
+            now = time.perf_counter()
+            elapsed = now - self._last_decode_tic
+            if elapsed > 0:
+                throughput = self._decode_tokens_since_last / elapsed
+            else:
+                throughput = 0.0
+            self._decode_tokens_since_last = 0
+            self._last_decode_tic = now
+
+        msg = (
+            "Decode batch, "
+            f"dp_rank: {self.dp_rank}, "
+            f"#running-req: {running_cnt}, "
+            f"#token: {tokens_used}, "
+            f"token usage: {token_usage:.2f}, "
+            f"cuda graph: {use_cudagraph}, "
+            f"gen throughput (token/s): {throughput:.2f}, "
+            f"#queue-req: {queue_cnt}, "
+        )
+        self._logger.info(msg)
diff --git a/fastdeploy/output/token_processor.py b/fastdeploy/output/token_processor.py
index 73c20dd457..e499e7b54a 100644
--- a/fastdeploy/output/token_processor.py
+++ b/fastdeploy/output/token_processor.py
@@ -66,6 +66,7 @@ class TokenProcessor:
         self.cfg = cfg
         self.cached_generated_tokens = cached_generated_tokens
         self.resource_manager = None
+        self.scheduler_metrics_logger = None
         self.engine_worker_queue = engine_worker_queue
         self.tokens_counter = Counter()
         self.split_connector = split_connector
@@ -160,6 +161,16 @@ class TokenProcessor:
         assert self.resource_manager is None, "The resource manager is not None, cannot set again."
         self.resource_manager = resource_manager
 
+    def set_scheduler_metrics_logger(self, scheduler_metrics_logger):
+        self.scheduler_metrics_logger = scheduler_metrics_logger
+
+    def _is_decode_stage(self, task):
+        if task is None:
+            return False
+        if task.need_prefill_tokens is None:
+            return False
+        return task.num_computed_tokens >= task.need_prefill_tokens
+
     def run(self):
         """
         start thread to get tokens
@@ -790,6 +801,9 @@ class TokenProcessor:
                             self.resource_manager.reschedule_preempt_task(task_id)
                     continue
 
+            if self.scheduler_metrics_logger and self._is_decode_stage(task):
+                self.scheduler_metrics_logger.on_decode_tokens(len(token_ids))
+
             if task.get("prefill_chunk_info", None) is not None:
                 prefill_chunk_num = task.get("prefill_chunk_num", 0)
                 task.prefill_chunk_num = prefill_chunk_num + 1
diff --git a/tests/engine/test_scheduler_metrics_logger.py b/tests/engine/test_scheduler_metrics_logger.py
new file mode 100644
index 0000000000..35daeb291c
--- /dev/null
+++ b/tests/engine/test_scheduler_metrics_logger.py
@@ -0,0 +1,74 @@
+"""
+Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import types
+from unittest import mock
+
+from fastdeploy.engine.sched.scheduler_metrics_logger import SchedulerMetricsLogger
+
+
+def test_on_decode_tokens_accumulates():
+    logger = SchedulerMetricsLogger(enabled=True, dp_rank=0)
+    logger._decode_tokens_since_last = 0
+
+    logger.on_decode_tokens(3)
+    logger.on_decode_tokens(0)
+    logger.on_decode_tokens(-1)
+
+    assert logger._decode_tokens_since_last == 3
+
+
+def test_log_prefill_batch_logs_expected_message():
+    logger = SchedulerMetricsLogger(enabled=True, dp_rank=2)
+    logger._logger = mock.Mock()
+
+    reqs = [
+        types.SimpleNamespace(prefill_start_index=0, prefill_end_index=4, num_cached_tokens=2),
+        types.SimpleNamespace(prefill_start_index=3, prefill_end_index=3, num_cached_tokens=1),
+    ]
+
+    logger.log_prefill_batch(prefill_reqs=reqs, running_cnt=5, queue_cnt=6, tokens_used=10, token_usage=0.75)
+
+    logger._logger.info.assert_called_once()
+    message = logger._logger.info.call_args[0][0]
+    assert "Prefill batch" in message
+    assert "dp_rank: 2" in message
+    assert "#new-seq: 2" in message
+    assert "#new-token: 4" in message
+    assert "#cached-token: 3" in message
+    assert "token usage: 0.75" in message
+    assert "#running-req: 5" in message
+    assert "#queue-req: 6" in message
+
+
+def test_log_decode_batch_computes_throughput(monkeypatch):
+    logger = SchedulerMetricsLogger(enabled=True, dp_rank=1)
+    logger._logger = mock.Mock()
+    logger._decode_tokens_since_last = 10
+    logger._last_decode_tic = 1.0
+
+    monkeypatch.setattr("fastdeploy.engine.sched.scheduler_metrics_logger.time.perf_counter", lambda: 3.0)
+
+    logger.log_decode_batch(running_cnt=4, queue_cnt=7, tokens_used=8, token_usage=0.5, use_cudagraph=True)
+
+    logger._logger.info.assert_called_once()
+    message = logger._logger.info.call_args[0][0]
+    assert "Decode batch" in message
+    assert "dp_rank: 1" in message
+    assert "gen throughput (token/s): 5.00" in message
+    assert "#queue-req: 7" in message
+    assert logger._decode_tokens_since_last == 0
+    assert logger._last_decode_tic == 3.0
diff --git a/tests/output/test_process_batch_output.py b/tests/output/test_process_batch_output.py
index 96e58bd521..6859259387 100644
--- a/tests/output/test_process_batch_output.py
+++ b/tests/output/test_process_batch_output.py
@@ -146,6 +146,7 @@ class TestTokenProcessorProcessBatchOutput(unittest.TestCase):
         processor.engine_worker_queue = Mock()
         processor.split_connector = Mock()
         processor.resource_manager = MockResourceManager()
+        processor.scheduler_metrics_logger = None
         task1 = MockTask()
         task2 = MockTask()
         processor.resource_manager.tasks_list = [task1, task2]
diff --git a/tests/output/test_token_processor.py b/tests/output/test_token_processor.py
index c30c39392e..e8ff821a26 100644
--- a/tests/output/test_token_processor.py
+++ b/tests/output/test_token_processor.py
@@ -199,6 +199,21 @@ def test_init_allocates_expected_buffers():
     processor_logprob, _, _, _ = _make_processor(enable_logprob=True)
     assert list(processor_logprob.output_scores.shape) == [MAX_BSZ * (K + 1), 1]
 
+
+def test_is_decode_stage():
+    processor, _, _, _ = _make_processor()
+
+    assert processor._is_decode_stage(None) is False
+
+    task = types.SimpleNamespace(need_prefill_tokens=None, num_computed_tokens=0)
+    assert processor._is_decode_stage(task) is False
+
+    task = types.SimpleNamespace(need_prefill_tokens=4, num_computed_tokens=3)
+    assert processor._is_decode_stage(task) is False
+
+    task = types.SimpleNamespace(need_prefill_tokens=4, num_computed_tokens=4)
+    assert processor._is_decode_stage(task) is True
+
     processor_spec, _, _, _ = _make_processor(speculative_method="mtp", enable_logprob=False)
     assert processor_spec.output_tokens.shape[0] == SPECULATE_MAX_BSZ * MAX_DRAFT_TOKENS + SPECULATE_MAX_BSZ + 2