diff --git a/swift/megatron/utils/megatron_lm_utils.py b/swift/megatron/utils/megatron_lm_utils.py
index 1710d333aa..d7a7b16247 100644
--- a/swift/megatron/utils/megatron_lm_utils.py
+++ b/swift/megatron/utils/megatron_lm_utils.py
@@ -82,6 +82,26 @@ def _initialize_mpu(args):
                         f'VPP: {args.virtual_pipeline_model_parallel_size}, CP: {args.context_parallel_size}, '
                         f'EP: {args.expert_model_parallel_size}, ETP: {args.expert_tensor_parallel_size}')
 
+        # before model weights are loaded onto GPU.
+        #
+        # Background: PyTorch lazily initializes NCCL communicators — dist.new_group() only registers
+        # metadata; the actual ncclCommInitRankConfig bootstrap runs on first collective use.
+        # For MoE models this group's first use is the very first optimizer step, by which point GPU
+        # memory is near its limit (~125-130 GiB on H200). If any rank cannot allocate the NCCL
+        # temporary buffer at that moment, its bootstrap thread stalls silently — NCCL_TIMEOUT does
+        # NOT cover the bootstrap phase — and all other ranks wait forever (deadlock).
+        #
+        # Calling a barrier here forces bootstrap while GPU memory is still empty, eliminating the race.
+        # get_inter_distributed_optimizer_instance_group returns _INTER_PARTIAL_EXPERT_DATA_PARALLEL_GROUP,
+        # which is None for dense models or when EP=1, so the guard is safe in all configurations.
+        if hasattr(mpu, 'get_inter_distributed_optimizer_instance_group'):
+            inter_ep_dp_group = mpu.get_inter_distributed_optimizer_instance_group(check_initialized=False)
+            if inter_ep_dp_group is not None:
+                logger.info('Pre-initializing INTER_PARTIAL_EXPERT_DATA_PARALLEL_GROUP NCCL communicator '
+                            'to avoid lazy-init deadlock during first optimizer step.')
+                torch.distributed.barrier(group=inter_ep_dp_group, device_ids=[torch.cuda.current_device()])
+                torch.cuda.synchronize()
+
 
 def initialize_megatron(args):
     # Pytorch distributed.