diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
index 136a6462..de94bad7 100644
--- a/vllm/model_executor/layers/quantization/fp8.py
+++ b/vllm/model_executor/layers/quantization/fp8.py
@@ -20,16 +20,16 @@ logger = init_logger(__name__)
 def cutlass_fp8_supported() -> bool:
     capability = torch.cuda.get_device_capability()
     capability = capability[0] * 10 + capability[1]
-    version = torch.version.cuda
-    version = version[0] * 10 + version[1]
+    major, minor = torch.version.cuda.split(".")
+    version = int(major) * 10 + int(minor)
 
     # CUTLASS FP8 kernels need at least
     #   CUDA 12.0 on SM90 systems (Hopper)
     #   CUDA 12.4 on SM89 systems (Lovelace)
     gpu_is_supported = False
-    if capability >= 900:
+    if capability >= 90:
         gpu_is_supported = version > 120
-    elif capability >= 890:
+    elif capability >= 89:
         gpu_is_supported = version > 124
 
     return gpu_is_supported
@@ -103,7 +103,7 @@ class Fp8LinearMethod(LinearMethodBase):
     1. Only support per-tensor quantization due to torch._scaled_mm support.
     2. Only support float8_e4m3fn data type due to the limitation of
        torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
-       
+
     Args:
         quant_config: The quantization config.
     """
@@ -298,8 +298,8 @@ class Fp8KVCacheMethod(QuantizeMethodBase):
         self.quant_config = quant_config
 
     def create_weights(self, layer: torch.nn.Module):
-        """Create "weight" (aka kv_scale) for an attention layer. 
-        
+        """Create "weight" (aka kv_scale) for an attention layer.
+
         Args:
             layer: The layer that is using the QuantizeMethodBase factory.
         """