diff --git a/torchtitan/distributed/utils.py b/torchtitan/distributed/utils.py
index 7790ab6683..cfe5fc19b3 100644
--- a/torchtitan/distributed/utils.py
+++ b/torchtitan/distributed/utils.py
@@ -250,24 +250,19 @@ def maybe_enable_amp(
     parallel_dims: ParallelDims, mixed_precision_param: str, device_type: str
 ) -> contextlib.AbstractContextManager[None]:
     if parallel_dims.fsdp_enabled:
-        # FSDP handles mixed precision internally
+        # FSDP handles mixed precision internally via MixedPrecisionPolicy
         logger.info("Mixed precision training is handled by fully_shard")
         return contextlib.nullcontext()
     else:
-        if parallel_dims.tp_enabled or parallel_dims.pp_enabled:
-            logger.warning(
-                "Mixed precision training with TP or PP is only supported when FSDP/HSDP/CP is enabled."
-            )
-            logger.info("Mixed precision training is disabled")
-            return contextlib.nullcontext()
-        else:
-            # the following code will only be executed for DDP or single-device training
-            logger.info("Mixed precision training is handled by AMP")
-            # pyrefly: ignore [bad-return]
-            return torch.autocast(
-                device_type,
-                dtype=TORCH_DTYPE_MAP[mixed_precision_param],
-            )
+        # Enable autocast for non-FSDP cases (DDP, TP-only, single-device)
+        # Note: PP uses its own schedule-based execution and doesn't use this context
+        # torch.autocast works at the operator level and is orthogonal to parallelism strategy
+        logger.info("Mixed precision training is handled by AMP")
+        # pyrefly: ignore [bad-return]
+        return torch.autocast(
+            device_type,
+            dtype=TORCH_DTYPE_MAP[mixed_precision_param],
+        )
 
 
 def init_fake_mode(world_size: int, comm_mode: str = "fake_backend"):