diff --git a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
index c6f914febc0a2..9fb194767e4a4 100644
--- a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
@@ -6,14 +6,22 @@ import torch
 
 
 def calculate_tile_tokens_dim(num_tokens, top_k, num_experts):
-    from flashinfer import next_positive_power_of_2
 
-    # Guess tokens per expert assuming perfect expert distribution first.
-    num_tokens_per_expert = (num_tokens * top_k) // num_experts
-    # And pad the number to the next power of 2.
-    tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
-    # Cap to 8-64 tokens per CTA tile as it's the range supported by the kernel.
-    tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+    # FlashInfer 0.2.10 has issues with larger tile sizes. Set to 8 for now.
+    # TODO: Revert this to dynamic calculation once a new version of FlashInfer
+    # with the necessary kernels is released.
+    tile_tokens_dim = 8
+
+    # from flashinfer import next_positive_power_of_2
+
+    # # Guess tokens per expert assuming perfect expert distribution first.
+    # num_tokens_per_expert = (num_tokens * top_k) // num_experts
+    # # And pad the number to the next power of 2.
+    # tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
+    # # Cap to 8-64 tokens per CTA tile as it's the range supported by the
+    # # kernel.
+    # tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+
     return tile_tokens_dim
 
 
diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py
index 1fcb190286329..c85d8bce31f5d 100755
--- a/vllm/v1/attention/backends/flashinfer.py
+++ b/vllm/v1/attention/backends/flashinfer.py
@@ -524,7 +524,8 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         head_dim = self.kv_cache_spec.head_size
 
         # currently prefill trtllm attention does not support fp8 kv cache
-        prefill_use_trtllm = use_trtllm_attention(
+        prefill_use_trtllm = not cache_dtype.startswith("fp8") \
+                                and use_trtllm_attention(
                                 num_prefill_tokens, max_seq_len, cache_dtype,
                                 num_qo_heads, num_kv_heads, head_dim)
         decode_use_trtllm = use_trtllm_attention(