diff --git a/server/text_generation_server/models/galactica.py b/server/text_generation_server/models/galactica.py
index b4d1c55..484621d 100644
--- a/server/text_generation_server/models/galactica.py
+++ b/server/text_generation_server/models/galactica.py
@@ -96,6 +96,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
         input_lengths = []
 
         # Parse batch
+        max_sequence_length = 0
+        padding_right_offset = 0
         for r in pb.requests:
             # Add escape_custom_split_sequence to the CausalLMBatch logic
             inputs.append(escape_custom_split_sequence(r.inputs))
@@ -103,8 +105,13 @@ class GalacticaCausalLMBatch(CausalLMBatch):
             next_token_choosers.append(
                 NextTokenChooser.from_pb(r.parameters, len(tokenizer), device)
             )
-            stopping_criterias.append(
-                StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            max_sequence_length = max(max_sequence_length, r.input_length)
+            padding_right_offset = max(
+                padding_right_offset, stopping_criteria.max_new_tokens
             )
 
         # Tokenize batch
@@ -114,6 +121,14 @@ class GalacticaCausalLMBatch(CausalLMBatch):
             padding=True,
             return_token_type_ids=False,
         ).to(device)
+        input_ids = tokenized_inputs["input_ids"]
+        # Allocate maximum attention_mask
+        attention_mask = input_ids.new_zeros(
+            (pb.size, max_sequence_length + padding_right_offset)
+        )
+        # Copy tokenizer attention_mask into fully allocated attention_mask
+        attention_mask[:, :max_sequence_length] = tokenized_inputs["attention_mask"]
+
         position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
         position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
         all_input_ids = tokenized_inputs["input_ids"].unsqueeze(-1)
@@ -121,8 +136,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
         return cls(
             batch_id=pb.id,
             requests=pb.requests,
-            input_ids=tokenized_inputs["input_ids"],
-            attention_mask=tokenized_inputs["attention_mask"],
+            input_ids=input_ids,
+            attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=None,
             all_input_ids=all_input_ids,
@@ -130,7 +145,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
             next_token_choosers=next_token_choosers,
             stopping_criterias=stopping_criterias,
             size=pb.size,
-            max_sequence_length=max(input_lengths),
+            max_sequence_length=max_sequence_length,
+            padding_right_offset=padding_right_offset,
         )