diff --git a/torchtitan/parallelisms/parallelize_llama.py b/torchtitan/parallelisms/parallelize_llama.py
index ed23936b..ac0f6c3b 100644
--- a/torchtitan/parallelisms/parallelize_llama.py
+++ b/torchtitan/parallelisms/parallelize_llama.py
@@ -76,6 +76,7 @@ def parallelize_llama(
                 "Please use rmsnorm or layernorm."
             )
         apply_compile(model)
+        print('after compile', model)
 
     if (
         parallel_dims.dp_shard_enabled
@@ -306,11 +307,23 @@ def apply_compile(model: nn.Module):
     Apply torch.compile to each TransformerBlock, which makes compilation efficient due to
     repeated structure. Alternatively one can compile the whole model (after applying DP).
     """
-    for layer_id, transformer_block in model.layers.named_children():
-        transformer_block = torch.compile(transformer_block, fullgraph=True)
-        model.layers.register_module(layer_id, transformer_block)
 
-    logger.info("Compiling each TransformerBlock with torch.compile")
+    # each transformer block
+    if False:
+        for layer_id, transformer_block in model.layers.named_children():
+            transformer_block = torch.compile(transformer_block, fullgraph=True)
+            model.layers.register_module(layer_id, transformer_block)
+        logger.info("Compiling each TransformerBlock with torch.compile")
+
+    # individual linear
+    if True:
+        for name, child in model.named_children():
+            if isinstance(child, torch.nn.Linear):
+                new_child = torch.compile(child)
+                setattr(model, name, new_child)
+            else:
+                apply_compile(child)
+        logger.info("Compiling each linear with torch.compile")
 
 
 def apply_fsdp(