diff --git a/QEfficient/generation/text_generation_inference.py b/QEfficient/generation/text_generation_inference.py
index f1fe5565..3eecd7b3 100755
--- a/QEfficient/generation/text_generation_inference.py
+++ b/QEfficient/generation/text_generation_inference.py
@@ -37,6 +37,7 @@ class PerfMetrics:
     decode_perf: float
     total_perf: float
     total_time: float
+    is_matching: Optional[bool]
 
 
 @dataclass
@@ -836,6 +837,44 @@ def generate_decode_stream(self, decode_inputs, generation_len):
                 break
         yield decode_inputs["input_ids"]  # yield the last token
 
+    def is_spd_acceptance_rate_fully_matching(self):
+
+        batch_size = self.generated_ids.shape[0]
+        valid_batch_indices = list(range(batch_size))
+        inputs = dict( 
+            input_ids = np.zeros((batch_size, self._decode_seq_len)),
+            position_ids = np.hstack([self.decode_pos_ids-self._decode_seq_len+i for i in range(self._decode_seq_len)]),
+            batch_index = np.arange(batch_size).reshape(-1,1)
+        )
+        i = 0
+        while True:
+            # Prepare inputs.
+            inputs["input_ids"] = self.generated_ids[valid_batch_indices, i:i+self._decode_seq_len]
+            inputs["position_ids"] = inputs["position_ids"][valid_batch_indices]+self._decode_seq_len
+            # Remove `valid_batch_indices` that pass ctx_len limit or have eos token.
+            passed_max_limit = (inputs["position_ids"] > self._ctx_len-1).any(1)
+            passed_indices = np.where(passed_max_limit)[0].tolist()
+            contains_eos = (inputs["input_ids"] == self.tokenizer.eos_token_id).any(1)
+            eos_indices = np.where(contains_eos)[0].tolist()
+            rm_indices = np.unique(np.concatenate((passed_indices, eos_indices))).tolist()
+            if rm_indices:
+                rm_indices = sorted(rm_indices)[::-1]
+                for rm_indice in rm_indices:
+                    del valid_batch_indices[rm_indice]
+            if not valid_batch_indices:
+                break
+            # Get predictions and perform validation step.
+            outputs = self._session.run(inputs)
+            input_ids = outputs["logits"][valid_batch_indices].argmax(2) # shape: [len(valid_batch_indices), self._decode_seq_len]
+            matching = (input_ids == self.generated_ids[valid_batch_indices, i+1:i+1+self._decode_seq_len])
+            if not matching.all():
+                logger.critical(f"TLM model does not have a 100% acceptance rate with itself!")
+                return False
+            i += self._decode_seq_len
+
+        return True
+
+
 
 class TextGeneration:
     def __init__(
@@ -931,7 +970,13 @@ def _regular_model_execution(
         prefill_time, decode_perf, total_perf, total_time = calculate_latency(
             total_decode_tokens, loop_start, start, end
         )
-        self._perf_metrics = PerfMetrics(prefill_time, decode_perf, total_perf, total_time)
+        # Find whether SpD acceptance rate is fully matching (100% acceptance rate).
+        is_matching = None
+        if self.is_tlm:
+            is_matching = self._qaic_model.is_spd_acceptance_rate_fully_matching()
+            if not is_matching:
+                logger.warning(f"SpD TLM model acceptance rate with itself is not 100%!")
+        self._perf_metrics = PerfMetrics(prefill_time, decode_perf, total_perf, total_time, is_matching)
         return self._perf_metrics, generated_texts
 
     def _continuous_batching_execution(