diff --git a/vllm/model_executor/layers/rejection_sampler.py b/vllm/model_executor/layers/rejection_sampler.py
index f173cbde03f44..9d6c3797c62fc 100644
--- a/vllm/model_executor/layers/rejection_sampler.py
+++ b/vllm/model_executor/layers/rejection_sampler.py
@@ -9,6 +9,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.spec_decode_base_sampler import (
     SpecDecodeStochasticBaseSampler)
+from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
@@ -368,7 +369,7 @@ def _smallest_positive_value(self) -> float:
 # Note that we always sample with replacement.
 # probs will be modified in place, but this is fine, as we pass
 # in a copy already.
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def _multinomial(
     probs: torch.Tensor,
     num_samples: int,
diff --git a/vllm/model_executor/layers/vocab_parallel_embedding.py b/vllm/model_executor/layers/vocab_parallel_embedding.py
index 30548e656c557..65920aa61ba15 100644
--- a/vllm/model_executor/layers/vocab_parallel_embedding.py
+++ b/vllm/model_executor/layers/vocab_parallel_embedding.py
@@ -133,7 +133,7 @@ def __post_init__(self):
         assert self.num_added_elements <= self.num_added_elements_padded
 
 
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def get_masked_input_and_mask(
         input_: torch.Tensor, org_vocab_start_index: int,
         org_vocab_end_index: int, num_org_vocab_padding: int,
diff --git a/vllm/model_executor/models/commandr.py b/vllm/model_executor/models/commandr.py
index d22d1f3171463..8d61ece289412 100644
--- a/vllm/model_executor/models/commandr.py
+++ b/vllm/model_executor/models/commandr.py
@@ -45,6 +45,7 @@
     row_parallel_weight_loader)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
@@ -53,7 +54,7 @@
                     maybe_prefix)
 
 
-@torch.compile
+@torch.compile(backend=current_platform.simple_compile_backend)
 def layer_norm_func(hidden_states, weight, variance_epsilon):
     input_dtype = hidden_states.dtype
     hidden_states = hidden_states.to(torch.float32)
diff --git a/vllm/model_executor/models/phi3_small.py b/vllm/model_executor/models/phi3_small.py
index da7e4cdbc6940..f47676b934e4e 100644
--- a/vllm/model_executor/models/phi3_small.py
+++ b/vllm/model_executor/models/phi3_small.py
@@ -20,6 +20,7 @@
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
@@ -54,12 +55,12 @@ def weight_loader(self, param: torch.nn.Parameter,
         return load_column_parallel_weight(param, loaded_weight)
 
 
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def quick_gelu(x):
     return x * torch.sigmoid(1.702 * x)
 
 
-@torch.compile(dynamic=True)
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def gegelu(input, limit: Optional[float] = None):
     a_gelu, a_linear = input[..., ::2], input[..., 1::2]
     if limit is not None:
diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py
index 01d753408e6d0..fe398801c5dd9 100644
--- a/vllm/platforms/interface.py
+++ b/vllm/platforms/interface.py
@@ -82,6 +82,12 @@ class Platform:
     # check https://github.com/pytorch/pytorch/blob/313dac6c1ca0fa0cde32477509cce32089f8532a/torchgen/model.py#L134 # noqa
     # use "CPU" as a fallback for platforms not registered in PyTorch
     dispatch_key: str = "CPU"
+    # The torch.compile backend for compiling simple and
+    # standalone functions. The default value is "inductor" to keep
+    # the same behavior as PyTorch.
+    # NOTE: for the forward part of the model, vLLM has another separate
+    # compilation strategy.
+    simple_compile_backend: str = "inductor"
     supported_quantization: list[str] = []
 
     def is_cuda(self) -> bool: