diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
index ab2d3313..cb777010 100644
--- a/server/text_generation_server/models/flash_causal_lm.py
+++ b/server/text_generation_server/models/flash_causal_lm.py
@@ -782,7 +782,9 @@ class FlashCausalLM(Model):
                 logger.info("Experimental support for Cuda Graphs is enabled")
                 # Warmup cuda graphs for all power of twos until 64
                 for i in range(6):
-                    self.cuda_graph_warmup(2**i, max_s, max_bt)
+                    bs = 2**i
+                    if self.speculate is None or self.speculate + 1 <= bs:
+                        self.cuda_graph_warmup(bs, max_s, max_bt)
             except Exception:
                 logger.exception(f"Decode cuda graph warmup failed")
 
@@ -840,13 +842,13 @@ class FlashCausalLM(Model):
             max_s = batch.max_seqlen
             lm_head_indices = batch.prefill_head_indices
 
-        bs = batch.input_ids.shape[0]
+        bs = input_ids.shape[0]
         # Ceil next power of two for batch size
         bs_next_power_of_two = 2 ** math.ceil(math.log2(bs))
         # Try to find an associated cuda graph
         cuda_graph = self.cuda_graphs.get(bs_next_power_of_two, None)
 
-        if batch.cu_seqlen_prefill is not None or cuda_graph is None:
+        if cu_seqlen_prefill is not None or cuda_graph is None:
             return self.model.forward(
                 input_ids=input_ids,
                 position_ids=position_ids,
diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py
index dee272a0..8d7e2a2b 100644
--- a/server/text_generation_server/models/flash_mistral.py
+++ b/server/text_generation_server/models/flash_mistral.py
@@ -446,13 +446,13 @@ class BaseFlashMistral(FlashCausalLM):
         if self.model.max_past is not None:
             max_s = min(self.model.max_past, max_s)
 
-        bs = batch.input_ids.shape[0]
+        bs = input_ids.shape[0]
         # Ceil next power of two for batch size
         bs_next_power_of_two = 2 ** math.ceil(math.log2(bs))
         # Try to find an associated cuda graph
         cuda_graph = self.cuda_graphs.get(bs_next_power_of_two, None)
 
-        if batch.cu_seqlen_prefill is not None or cuda_graph is None:
+        if cu_seqlen_prefill is not None or cuda_graph is None:
             logits = self.model.forward(
                 input_ids=input_ids,
                 position_ids=position_ids,