diff --git a/mbridge/peft/lora.py b/mbridge/peft/lora.py
index 1c5e97b..a8cc51f 100644
--- a/mbridge/peft/lora.py
+++ b/mbridge/peft/lora.py
@@ -649,17 +649,19 @@ def gather_lora_state_dict(models, bridge=None) -> Dict[str, torch.Tensor]:
                 continue
 
             # --- LinearAdapter (nn.Linear with LoRA, for ViT/projector) ---
+            # CPU init + average_gradients_across_tp_domain guarantees all
+            # TP ranks hold identical weights, so no broadcast is needed.
             if isinstance(module, LinearAdapter):
-                lin_in_w = module.linear_in.weight.data.cpu()
-                lin_out_w = module.linear_out.weight.data.cpu()
+                lin_in_w = module.linear_in.weight.data
+                lin_out_w = module.linear_out.weight.data
                 hf_key = mcore_adapter_name_to_hf(
                     f"{name}.linear_in.weight", bridge=bridge,
                 )
-                adapter_state[hf_key] = lin_in_w
+                adapter_state[hf_key] = lin_in_w.cpu()
                 hf_key = mcore_adapter_name_to_hf(
                     f"{name}.linear_out.weight", bridge=bridge,
                 )
-                adapter_state[hf_key] = lin_out_w
+                adapter_state[hf_key] = lin_out_w.cpu()
                 continue
 
             # --- Standard LoRA ---
@@ -1029,7 +1031,59 @@ def transform(self, module: nn.Module, name: Optional[str] = None, prefix: Optio
         # Detect stride for strided ColumnParallelLinear (gated MLP)
         stride = getattr(module.to_wrap, 'stride', 1)
 
-        if hasattr(module.to_wrap, "weight"):
+        # Detect interleaved TP layout for linear-attention in_proj.
+        # The in_proj ColumnParallelLinear has a non-standard TP layout
+        # where each component (wq, wk, wv, wz, wb, wa) is sharded
+        # independently and concatenated per-rank, rather than a simple
+        # contiguous row split.  We detect this by checking if the full
+        # output dimension matches the in_proj formula:
+        #   in_proj_dim = k_dim*2 + v_dim*2 + num_v_heads*2
+        config = getattr(module.to_wrap, 'config', None)
+        has_interleaved_in_proj = False
+        if config is not None and tp_size > 1:
+            lnkh = getattr(config, 'linear_num_key_heads', None)
+            lkhd = getattr(config, 'linear_key_head_dim', None)
+            lnvh = getattr(config, 'linear_num_value_heads', None)
+            lvhd = getattr(config, 'linear_value_head_dim', None)
+            if all(v is not None for v in (lnkh, lkhd, lnvh, lvhd)):
+                in_proj_dim = (
+                    lnkh * lkhd * 2 + lnvh * lvhd * 2 + lnvh * 2
+                )
+                full_out = module.to_wrap.weight.shape[0] * tp_size
+                if full_out == in_proj_dim:
+                    has_interleaved_in_proj = True
+
+        if has_interleaved_in_proj:
+            # Compute the full (un-sharded) delta using _compute_sub_delta,
+            # then split by component sizes and reassemble in the
+            # interleaved per-TP-rank layout.
+            base_device = module.to_wrap.weight.device
+            full_delta = self._compute_sub_delta(
+                module.adapter.linear_out,
+                module.adapter.linear_in,
+                module.adapter.alpha,
+                module.adapter.dim,
+                base_device,
+            )
+            # Component sizes (full, not per-TP-rank)
+            k_dim = config.linear_num_key_heads * config.linear_key_head_dim
+            v_dim = config.linear_num_value_heads * config.linear_value_head_dim
+            num_v_heads = config.linear_num_value_heads
+            split_shape = [k_dim, k_dim, v_dim, v_dim, num_v_heads, num_v_heads]
+            # Split full delta into 6 components: [wq, wk, wv, wz, wb, wa]
+            delta_parts = full_delta.split(split_shape, dim=0)
+            assert len(delta_parts) == 6, (
+                f"in_proj expected 6 components, got {len(delta_parts)}"
+            )
+            # Split each component by TP size and reassemble per-rank
+            per_rank_delta = torch.cat(
+                [part.chunk(tp_size, dim=0)[tp_rank] for part in delta_parts],
+                dim=0,
+            )
+            module.to_wrap.weight.data = (
+                module.to_wrap.weight.data + per_rank_delta.to(base_device)
+            )
+        elif hasattr(module.to_wrap, "weight"):
             base_device = module.to_wrap.weight.device
             merged_weight = self.merge(
                 module.to_wrap.weight,
@@ -1168,6 +1222,8 @@ def lora_merged(models):
                 continue
             _backup_tensor_to_cpu(la_module.weight)
             scale = la_module.scale
+            # CPU init + average_gradients_across_tp_domain guarantees all
+            # TP ranks hold identical weights, so no broadcast is needed.
             delta = la_module.linear_out.weight.data @ la_module.linear_in.weight.data
             la_module.weight.data.add_(scale * delta)
             # Hide LoRA sub-modules so they don't appear in named_parameters()
diff --git a/mbridge/peft/lora_layers.py b/mbridge/peft/lora_layers.py
index 965f2b7..930e2f2 100644
--- a/mbridge/peft/lora_layers.py
+++ b/mbridge/peft/lora_layers.py
@@ -136,6 +136,21 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
 class LoRATopKRouter(AdapterWrapper):
     """Adapter wrapper that applies LoRA to router gating logits."""
 
+    def __init__(self, to_wrap: nn.Module, adapter: nn.Module) -> None:
+        super().__init__(to_wrap, adapter)
+        # Mirror base router weight behavior (router.py line 84):
+        #   setattr(self.weight, 'sequence_parallel', self.config.sequence_parallel)
+        # The router adapter skips the SP gather for efficiency (each TP rank
+        # only routes its local seq/TP tokens).  When SP is enabled, the
+        # adapter's ColumnParallelLinear weights receive gradients from local
+        # tokens only.  Mark them so finalize_model_grads will SUM-allreduce
+        # across TP ranks, producing the correct full gradient.
+        seq_parallel = to_wrap.config.sequence_parallel
+        for sub in (adapter,) if not isinstance(adapter, nn.ModuleList) else adapter:
+            for p in sub.parameters():
+                if p.requires_grad:
+                    setattr(p, "sequence_parallel", seq_parallel)
+
     def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any):
         """Forward pass that adds LoRA delta to router logits before routing."""
         self.to_wrap._maintain_float32_expert_bias()
@@ -260,13 +275,30 @@ def _init_adapter(
         out_features = obj.out_features
         dtype = lora_dtype or obj.weight.dtype
 
-        obj.linear_in = nn.Linear(in_features, dim, bias=False, dtype=dtype, device=device)
-        obj.linear_out = nn.Linear(dim, out_features, bias=False, dtype=dtype, device=device)
+        # Initialize on CPU first to ensure TP-independent initialization,
+        # then move to target device.  GPU initialization uses per-rank
+        # model-parallel seeds, producing different LoRA-A weights on
+        # different TP ranks, which leads to divergent training and
+        # incorrect checkpoint export when TP > 1.
+        obj.linear_in = nn.Linear(in_features, dim, bias=False, dtype=dtype, device="cpu")
+        obj.linear_out = nn.Linear(dim, out_features, bias=False, dtype=dtype, device="cpu")
         if lora_A_init_method == "xavier":
             torch.nn.init.xavier_uniform_(obj.linear_in.weight.data)
         else:
             nn.init.kaiming_uniform_(obj.linear_in.weight.data, a=math.sqrt(5))
         obj.linear_out.weight.data.fill_(0)
+        # Move to target device after CPU initialization
+        obj.linear_in = obj.linear_in.to(device=device)
+        obj.linear_out = obj.linear_out.to(device=device)
+
+        # Mark LoRA parameters for TP gradient averaging.
+        # TELinearAdapter wraps TE layers that are replicated across TP
+        # ranks (not sharded like ColumnParallelLinear/RowParallelLinear).
+        # Without this, _allreduce_non_tensor_model_parallel_grads skips
+        # these parameters and their gradients diverge across TP ranks.
+        setattr(obj.linear_in.weight, "average_gradients_across_tp_domain", True)
+        setattr(obj.linear_out.weight, "average_gradients_across_tp_domain", True)
+
         if dropout > 0.0:
             obj.dropout = nn.Dropout(p=dropout)
         else:
@@ -664,13 +696,30 @@ def _init_adapter(
         out_features = obj.out_features
         dtype = lora_dtype or obj.weight.dtype
 
-        obj.linear_in = nn.Linear(in_features, dim, bias=False, dtype=dtype, device=device)
-        obj.linear_out = nn.Linear(dim, out_features, bias=False, dtype=dtype, device=device)
+        # Initialize on CPU first to ensure TP-independent initialization,
+        # then move to target device.  GPU initialization uses per-rank
+        # model-parallel seeds, producing different LoRA-A weights on
+        # different TP ranks, which leads to divergent training and
+        # incorrect checkpoint export when TP > 1.
+        obj.linear_in = nn.Linear(in_features, dim, bias=False, dtype=dtype, device="cpu")
+        obj.linear_out = nn.Linear(dim, out_features, bias=False, dtype=dtype, device="cpu")
         if lora_A_init_method == "xavier":
             torch.nn.init.xavier_uniform_(obj.linear_in.weight.data)
         else:
             nn.init.kaiming_uniform_(obj.linear_in.weight.data, a=math.sqrt(5))
         obj.linear_out.weight.data.fill_(0)
+        # Move to target device after CPU initialization
+        obj.linear_in = obj.linear_in.to(device=device)
+        obj.linear_out = obj.linear_out.to(device=device)
+
+        # Mark LoRA parameters for TP gradient averaging.
+        # LinearAdapter wraps nn.Linear layers that are replicated across TP
+        # ranks (not sharded like ColumnParallelLinear/RowParallelLinear).
+        # Without this, _allreduce_non_tensor_model_parallel_grads skips
+        # these parameters and their gradients diverge across TP ranks.
+        setattr(obj.linear_in.weight, "average_gradients_across_tp_domain", True)
+        setattr(obj.linear_out.weight, "average_gradients_across_tp_domain", True)
+
         if dropout > 0.0:
             obj.dropout = nn.Dropout(p=dropout)
         else: