diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
index a0159c39092c5..67e5b65de6010 100644
--- a/vllm/model_executor/layers/quantization/fp8.py
+++ b/vllm/model_executor/layers/quantization/fp8.py
@@ -77,9 +77,8 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
     is_layer_skipped,
-    kFp8DynamicTensorSym,
+    kFp8DynamicTokenSym,
     kFp8StaticTensorSym,
-    kFp8StaticTokenSym,
 )
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d,
@@ -381,10 +380,10 @@ class Fp8LinearMethod(LinearMethodBase):
             # Use per-token quantization for better perf if dynamic and cutlass
             if not self.act_q_static and cutlass_fp8_supported():
                 self.act_q_group_shape = GroupShape.PER_TOKEN
-                self.activation_quant_key = kFp8StaticTokenSym
+                self.activation_quant_key = kFp8DynamicTokenSym
             else:
                 self.act_q_group_shape = GroupShape.PER_TENSOR
-                self.activation_quant_key = kFp8DynamicTensorSym
+                self.activation_quant_key = kFp8StaticTensorSym
 
         if self.block_quant:
             assert not self.act_q_static
diff --git a/vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py b/vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py
index b033cc7905e4e..36e4a16c01683 100644
--- a/vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py
+++ b/vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py
@@ -69,6 +69,10 @@ _POSSIBLE_FP8_KERNELS: dict[PlatformEnum, list[type[FP8ScaledMMLinearKernel]]] =
         RowWiseTorchScaledMMLinearKernel,
         ChannelWiseTorchScaledMMLinearKernel,
     ],
+    PlatformEnum.CPU: [
+        PerTensorTorchScaledMMLinearKernel,
+        ChannelWiseTorchScaledMMLinearKernel,
+    ],
 }
 
 _KernelT = TypeVar("_KernelT", bound=ScaledMMLinearKernel)