diff --git a/torchtitan/models/deepseek_v3/infra/parallelize.py b/torchtitan/models/deepseek_v3/infra/parallelize.py
index 0793820ffd..c943978d30 100644
--- a/torchtitan/models/deepseek_v3/infra/parallelize.py
+++ b/torchtitan/models/deepseek_v3/infra/parallelize.py
@@ -118,7 +118,7 @@ def parallelize_deepseekv3(
         )
 
     if model_compile_enabled:
-        apply_compile(model, job_config.compile)
+        apply_compile(model, job_config.compile, parallel_dims.ep_enabled)
 
     dp_mesh: DeviceMesh | None = None
     if parallel_dims.fsdp_enabled or parallel_dims.ep_enabled:
diff --git a/torchtitan/models/llama4/infra/parallelize.py b/torchtitan/models/llama4/infra/parallelize.py
index 9911ecdfd0..0764652a85 100644
--- a/torchtitan/models/llama4/infra/parallelize.py
+++ b/torchtitan/models/llama4/infra/parallelize.py
@@ -129,7 +129,7 @@ def parallelize_llama(
 
     # turn on per-TransformerBlock compile after AC wrapping and before FSDP
     if model_compile_enabled:
-        apply_compile(model, job_config.compile)
+        apply_compile(model, job_config.compile, parallel_dims.ep_enabled)
 
     dp_mesh: DeviceMesh | None = None
     if parallel_dims.fsdp_enabled or parallel_dims.ep_enabled:
@@ -506,7 +506,7 @@ def apply_moe_ep_tp(
         )
 
 
-def apply_compile(model: nn.Module, compile_config: CompileConfig):
+def apply_compile(model: nn.Module, compile_config: CompileConfig, ep_enabled: bool):
     """
     Apply torch.compile to each TransformerBlock, which makes compilation efficient due to
     repeated structure. Alternatively one can compile the whole model (after applying DP).
@@ -577,6 +577,22 @@ def apply_compile(model: nn.Module, compile_config: CompileConfig):
         fullgraph=True,
     )
 
+    if ep_enabled:
+        compiled_fn = moe_module._run_experts_grouped_mm
+
+        def _run_experts_grouped_mm_dynamic(
+            w1: torch.Tensor,
+            w2: torch.Tensor,
+            w3: torch.Tensor,
+            x: torch.Tensor,
+            num_tokens_per_expert: torch.Tensor,
+        ) -> torch.Tensor:
+            # dynamic number of tokens in expert parallel
+            torch._dynamo.mark_dynamic(x, 0)
+            return compiled_fn(w1, w2, w3, x, num_tokens_per_expert)
+
+        moe_module._run_experts_grouped_mm = _run_experts_grouped_mm_dynamic
+
     # NOTE: We don't compile for loop code path due to an issue with unbacked symints:
     # https://github.com/pytorch/pytorch/issues/166460
 
diff --git a/torchtitan/models/qwen3/infra/parallelize.py b/torchtitan/models/qwen3/infra/parallelize.py
index 6b8dc3d5a6..adaa2ad3e8 100644
--- a/torchtitan/models/qwen3/infra/parallelize.py
+++ b/torchtitan/models/qwen3/infra/parallelize.py
@@ -119,7 +119,7 @@ def parallelize_qwen3(
 
     # turn on per-TransformerBlock compile after AC wrapping and before FSDP
     if model_compile_enabled:
-        apply_compile(model, job_config.compile)
+        apply_compile(model, job_config.compile, parallel_dims.ep_enabled)
 
     if parallel_dims.fsdp_enabled:
         # apply FSDP or HSDP, potentially with Context Parallel