diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index 36f70180..cf9ac405 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -1389,29 +1389,48 @@ class FlashCausalLM(Model):
         ]
 
     def cuda_graph_warmup(self, bs: int, max_s: int, max_bt: int):
-        input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
-        position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
-        slots = torch.arange(bs, dtype=torch.int64, device=self.device)
+        max_bs = max(self.cuda_graphs.keys()) if self.cuda_graphs else None
         input_lengths = [max_s] * bs
         cache_lengths = [0] * bs
-        input_lengths_tensor = (
-            torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
-        )
-        cache_lengths_tensor = torch.zeros(bs, dtype=torch.int32, device=self.device)
-        block_tables = torch.arange(
-            max_bt, dtype=torch.int32, device=self.device
-        ).repeat(bs)
-        block_tables = block_tables.reshape((bs, max_bt))
+        if max_bs is None:
+            input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
+            position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
+            slots = torch.arange(bs, dtype=torch.int64, device=self.device)
+            input_lengths_tensor = (
+                torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
+            )
+            cache_lengths_tensor = torch.zeros(
+                bs, dtype=torch.int32, device=self.device
+            )
+            block_tables = torch.arange(
+                max_bt, dtype=torch.int32, device=self.device
+            ).repeat(bs)
+            block_tables = block_tables.reshape((bs, max_bt))
+            if ATTENTION == "flashinfer":
+                block_tables = block_tables_to_ragged(
+                    block_tables=block_tables,
+                    input_lengths=input_lengths,
+                    cache_lengths=cache_lengths,
+                    input_lengths_tensor=input_lengths_tensor,
+                    cache_lengths_tensor=cache_lengths_tensor,
+                    max_current_length=max_s,
+                )
+        else:
+            if bs > max_bs:
+                raise RuntimeError(
+                    "Cuda graphs should be generated in decreasing order size to reduce VRAM usage"
+                )
+            input_ids = self.cuda_graphs[max_bs]["input_ids"][: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]
+            else:
+                block_tables = self.cuda_graphs[max_bs]["block_tables"][:bs]
+            slots = self.cuda_graphs[max_bs]["slots"][:bs]
+            input_lengths_tensor = self.cuda_graphs[max_bs]["input_lengths"][:bs]
+            cache_lengths_tensor = self.cuda_graphs[max_bs]["cache_lengths"][:bs]
 
         if ATTENTION == "flashinfer":
-            block_tables = block_tables_to_ragged(
-                block_tables=block_tables,
-                input_lengths=input_lengths,
-                cache_lengths=cache_lengths,
-                input_lengths_tensor=input_lengths_tensor,
-                cache_lengths_tensor=cache_lengths_tensor,
-                max_current_length=max_s,
-            )
             from text_generation_server.layers.attention.flashinfer import (
                 create_decode_state_cuda_graphs,
             )