diff --git a/text-generation-inference/server/text_generation_server/generator.py b/text-generation-inference/server/text_generation_server/generator.py
index 245ee64a..facd511d 100644
--- a/text-generation-inference/server/text_generation_server/generator.py
+++ b/text-generation-inference/server/text_generation_server/generator.py
@@ -449,10 +449,14 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
         empty_slots = slots[Slot.State.EMPTY]
         model_batch_size = self.model.config.batch_size
         if model_batch_size is not None and model_batch_size < len(active_slots) + len(batch.requests):
-            raise ValueError(
+            # If raising an error here wouldn't crash the server, we could raise a ValueError
+            error = ValueError(
                 f"Cannot prefill {len(batch.requests)} new request(s)."
                 f" Maximum batch size supported is: {model_batch_size}."
             )
+            # but since it's not possible, we just log the error and return an empty generation
+            logger.error(error)
+            return [], None
         for slot in empty_slots:
             self.slots.remove(slot)
         # Assign each request to an empty slot
@@ -505,13 +509,21 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
             input_ids[i, -truncation:] = tokenized_inputs.input_ids[i, -truncation:]
             slot_input_ids = input_ids[i : i + 1, :]
             # Padded input ids are also required to set logits processors and stopping criterias
-            selector = TokenSelector.create(
-                slot_input_ids,
-                slot.generation_config,
-                self.model,
-                self.model.config.sequence_length,
-                seed=slot.seed,
-            )
+            try:
+                selector = TokenSelector.create(
+                    slot_input_ids,
+                    slot.generation_config,
+                    self.model,
+                    self.model.config.sequence_length,
+                    seed=slot.seed,
+                )
+            except ValueError as e:
+                # This is very unlikely, but it seems it could be possible if router does not check values beforehand.
+                # In that case, we just skip the slot, and mark it as empty. This should prevent returning this to the
+                # router.
+                logger.error(f"Invalid generation parameters for slot {slot.id}. Skipping it. Error: {e}")
+                slot.clear()
+                continue
             slot_input_ids = slot_input_ids.squeeze(dim=0).type(torch.int64)
             attention_mask[i, -truncation:] = tokenized_inputs.attention_mask[i, -truncation:]
             if self._supports_static_cache: