diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py
index 796f8cd3..8ec6aca8 100644
--- a/server/text_generation_server/models/causal_lm.py
+++ b/server/text_generation_server/models/causal_lm.py
@@ -602,9 +602,12 @@ class CausalLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
+        self.prev_bs = 0
+
         # Create tokenizer
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
@@ -965,6 +968,9 @@ class CausalLM(Model):
             batch = batch.__class__.recombine([batch], self.tokenizer.pad_token_id)
 
         scenario = 'PREFILL' if prefill else 'GENERATE'
+        if self.enable_hpu_graph and self.limit_hpu_graph and round_up(batch.batch_size, BATCH_BUCKET_SIZE) != self.prev_bs:
+            self.model.clear_cache()
+            self.prev_bs = round_up(batch.batch_size, BATCH_BUCKET_SIZE)
         dbg_trace(
             scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length} padding:{batch.right_padding}')
         assert batch.right_padding > 0, 'No more room for next token!'
@@ -981,6 +987,10 @@ class CausalLM(Model):
                 batch.past_key_values,
                 bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
             )
+        elif all([req.stopping_criteria.max_new_tokens == 1 for req in batch.requests]):
+            # Don't schedule next forward if max_new_tokens for all requests equals 1 
+            # - we've already generated the first and only needed token in the prefill phase
+            pass
         else:
             token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
             input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1)