diff --git a/torchchat/cli/cli.py b/torchchat/cli/cli.py
index 007fe0c8f..d35c8efbd 100644
--- a/torchchat/cli/cli.py
+++ b/torchchat/cli/cli.py
@@ -359,6 +359,12 @@ def _add_generation_args(parser, verb: str) -> None:
             default=1,
             help="Number of samples",
         )
+        generator_parser.add_argument(
+            "--accumulate-tokens",
+            type=int,
+            default=8,
+            help="Number of generated tokens to accumulate before calling the callback on each one of them.",
+        )
 
     generator_parser.add_argument(
         "--image-prompts",
diff --git a/torchchat/generate.py b/torchchat/generate.py
index 53d9d8f8c..3d639bbe7 100644
--- a/torchchat/generate.py
+++ b/torchchat/generate.py
@@ -230,6 +230,7 @@ class GeneratorArgs:
     max_autotune: bool = False
     # (Misnomer) See Issue: https://github.com/pytorch/torchchat/issues/1273
     is_torchtune_model: bool = False
+    accumulate_tokens: int = 8
 
     def __post_init__(self):
         if self.compile_prefill and self.sequential_prefill:
@@ -294,6 +295,7 @@ def from_args(cls, args):
             sequential_prefill=sequential_prefill,
             max_autotune=args.max_autotune,
             is_torchtune_model=args.model and args.model.endswith("tune"),
+            accumulate_tokens=getattr(args, "accumulate_tokens", 8),
         )
 
 
@@ -530,11 +532,13 @@ def decode_n_tokens(
         need_probs: bool,
         batch=Optional[Dict[str, Any]],  # Inputs for multimodal models
         callback=lambda _: _,
+        accumulate_tokens: int = 8,
         eos_token_id: int = 2,
         eot_id: Optional[int] = None,
         attention_backend: SDPBackend = torch.nn.attention.SDPBackend.MATH,
         **sampling_kwargs,
     ):
+        new_tokens = []
         encountered_eos = False
         for _i in range(
             num_new_tokens - 1
@@ -552,29 +556,52 @@ def decode_n_tokens(
                     **sampling_kwargs,
                 )
                 input_pos += 1
-                callback(next_token.clone(), done_generating=_i == num_new_tokens - 2)
+                new_tokens.append(next_token.clone())
+
+                done_generating = _i == num_new_tokens - 2
+                if need_probs:
+                    callback(new_tokens[-1], done_generating=done_generating)
                 if not need_probs or next_prob is None:
                     yield out_token, None
                 else:
                     yield out_token, next_prob.clone()
                 cur_token = next_token
 
-                # encountered eos
-                if next_token.item() == eos_token_id or (
-                    eot_id is not None and next_token.item() == eot_id
-                ):
-                    encountered_eos = True
-                    final_token, next_prob = self.decode_one_token(
-                        model,
-                        cur_token,
-                        input_pos,
-                        need_probs,
-                        batch=batch,
-                        **sampling_kwargs,
-                    )
-                    input_pos += 1
-                    yield cur_token.clone(), next_prob.clone()
-                    break
+                if need_probs:
+                    # encountered eos
+                    if next_token.item() == eos_token_id or (
+                        eot_id is not None and next_token.item() == eot_id
+                    ):
+                        encountered_eos = True
+                        final_token, next_prob = self.decode_one_token(
+                            model,
+                            cur_token,
+                            input_pos,
+                            need_probs,
+                            batch=batch,
+                            **sampling_kwargs,
+                        )
+                        input_pos += 1
+                        yield cur_token.clone(), next_prob.clone()
+                        break
+                else:
+                    callback_pos = _i % accumulate_tokens + 1
+                    if done_generating or callback_pos == accumulate_tokens:
+                        callback_num = min(accumulate_tokens, callback_pos)
+                        for i in range(callback_num, 0, -1):
+                            callback(new_tokens[-i], done_generating=done_generating)
+
+                            token_item = new_tokens[-i].item()
+                            # encountered eos
+                            if token_item == eos_token_id or (
+                                eot_id is not None and token_item == eot_id
+                            ):
+                                encountered_eos = True
+                                input_pos += 1
+                                yield new_tokens[-i].clone(), None
+                                break
+                        if encountered_eos:
+                            break
 
         if not encountered_eos:
             eos_token = torch.tensor(
@@ -681,6 +708,7 @@ def generate(
         speculate_k: Optional[int] = 8,
         sequential_prefill=True,
         callback=lambda x: x,
+        accumulate_tokens: int,
         max_seq_length: int,
         attention_backend: SDPBackend = torch.nn.attention.SDPBackend.MATH,
         seed: Optional[int] = None,
@@ -791,6 +819,7 @@ def generate(
                 max_new_tokens - 1,
                 batch=batch,
                 callback=callback,
+                accumulate_tokens=accumulate_tokens,
                 need_probs=False,
                 eos_token_id=self.tokenizer.eos_id() if self.tokenizer else 2,
                 eot_id=(
@@ -1179,6 +1208,7 @@ def callback(x, *, done_generating=False):
                     chat_mode=generator_args.chat_mode,
                     batch=batch,
                     callback=callback,
+                    accumulate_tokens=generator_args.accumulate_tokens,
                     temperature=generator_args.temperature,
                     top_k=generator_args.top_k,
                     sequential_prefill=generator_args.sequential_prefill,