diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py
index 4be93148139d..1bba99088bb2 100644
--- a/vllm/platforms/cuda.py
+++ b/vllm/platforms/cuda.py
@@ -112,6 +112,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         parallel_config = vllm_config.parallel_config
         scheduler_config = vllm_config.scheduler_config
         compilation_config = vllm_config.compilation_config
+        model_config = vllm_config.model_config
 
         if parallel_config.worker_cls == "auto":
             if scheduler_config.is_multi_step:
@@ -142,14 +143,21 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         cache_config = vllm_config.cache_config
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 16
+
         # TODO(lucas): handle this more gracefully
-        if envs.VLLM_ATTENTION_BACKEND is not None \
-           and envs.VLLM_ATTENTION_BACKEND == "FLASHMLA" \
-           and cache_config.block_size != 64:
-            cache_config.block_size = 64
-            logger.info(
-                "FlashMLA: Forcing kv cache block size to 64 since this"
-                " is currently the only block size supported by the kernel.")
+        # Note: model_config may be None during testing
+        if model_config is not None and model_config.use_mla:
+            # if `VLLM_ATTENTION_BACKEND` is not set and we are using MLA, then
+            # we default to FlashMLA backend, so we need to force the blocksize
+            # here
+            use_flashmla = (envs.VLLM_ATTENTION_BACKEND is None \
+                or envs.VLLM_ATTENTION_BACKEND == "FLASHMLA")
+            from vllm.attention.backends.flashmla import is_flashmla_supported
+            if use_flashmla and is_flashmla_supported()[0] \
+                and cache_config.block_size != 64:
+                cache_config.block_size = 64
+                logger.info(
+                    "Forcing kv cache block size to 64 for FlashMLA backend.")
 
         if (parallel_config.data_parallel_size > 1
                 and compilation_config.use_cudagraph):
@@ -173,7 +181,15 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
         if use_mla:
             # TODO(lucas): refactor to  be more concise
             #  we should probably consider factoring out V1 here
-            if selected_backend == _Backend.FLASHMLA:
+            if selected_backend == _Backend.TRITON_MLA or block_size != 64:
+                if use_v1:
+                    logger.info_once("Using Triton MLA backend on V1 engine.")
+                    return ("vllm.v1.attention.backends.mla."
+                            "triton_mla.TritonMLABackend")
+                else:
+                    logger.info("Using Triton MLA backend.")
+                    return "vllm.attention.backends.triton_mla.TritonMLABackend"
+            else:
                 from vllm.attention.backends.flashmla import (
                     is_flashmla_supported)
                 if not is_flashmla_supported()[0]:
@@ -195,14 +211,6 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                         logger.info("Using FlashMLA backend.")
                         return ("vllm.attention.backends."
                                 "flashmla.FlashMLABackend")
-
-            if use_v1:
-                logger.info_once("Using Triton MLA backend on V1 engine.")
-                return ("vllm.v1.attention.backends.mla."
-                        "triton_mla.TritonMLABackend")
-            else:
-                logger.info("Using Triton MLA backend.")
-                return "vllm.attention.backends.triton_mla.TritonMLABackend"
         if use_v1:
             logger.info_once("Using Flash Attention backend on V1 engine.")
             return ("vllm.v1.attention.backends.flash_attn."