diff --git a/server/text_generation_server/models/vlm_causal_lm.py b/server/text_generation_server/models/vlm_causal_lm.py
index d8c5103f..46657a7a 100644
--- a/server/text_generation_server/models/vlm_causal_lm.py
+++ b/server/text_generation_server/models/vlm_causal_lm.py
@@ -834,7 +834,7 @@ class VlmCausalLM(FlashCausalLM):
         cache_lengths = [0] * bs
         if max_bs is None:
             input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
-            input_embeds = torch.zeros(
+            inputs_embeds = torch.zeros(
                 (bs, self.model.config.text_config.hidden_size),
                 device=self.device,
                 dtype=self.dtype,
@@ -873,7 +873,7 @@ class VlmCausalLM(FlashCausalLM):
                     "Cuda graphs should be generated in decreasing order size to reduce VRAM usage"
                 )
             input_ids = self.cuda_graphs[max_bs]["input_ids"][:bs]
-            input_embeds = self.cuda_graphs[max_bs]["input_embeds"][:bs]
+            inputs_embeds = self.cuda_graphs[max_bs]["inputs_embeds"][:bs]
             position_ids = self.cuda_graphs[max_bs]["position_ids"][:bs]
             if ATTENTION == "flashinfer":
                 block_tables = self.cuda_graphs[max_bs]["block_tables"][: bs * max_bt]
@@ -906,7 +906,7 @@ class VlmCausalLM(FlashCausalLM):
         graph = torch.cuda.CUDAGraph()
         self.cuda_graphs[bs] = {
             "input_ids": input_ids,
-            "input_embeds": input_embeds,
+            "inputs_embeds": inputs_embeds,
             "position_ids": position_ids,
             "kv_cache": self.kv_cache,
             "block_tables": block_tables,
@@ -935,7 +935,7 @@ class VlmCausalLM(FlashCausalLM):
             )
             self.model.forward(
                 input_ids=input_ids,
-                inputs_embeds=input_embeds,
+                inputs_embeds=inputs_embeds,
                 position_ids=position_ids,
                 cu_seqlen_prefill=None,
                 kv_cache=self.kv_cache,
@@ -960,7 +960,7 @@ class VlmCausalLM(FlashCausalLM):
                 )
                 logits, speculative_logits = self.model.forward(
                     input_ids=input_ids,
-                    inputs_embeds=input_embeds,
+                    inputs_embeds=inputs_embeds,
                     position_ids=position_ids,
                     cu_seqlen_prefill=None,
                     kv_cache=self.kv_cache,