diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 49ea86e6..e9c04ab5 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -165,7 +165,7 @@ def __init__(
             bias=False,
             async_communication=tp_linear_async_communication and tp_mode is TensorParallelLinearMode.REDUCE_SCATTER,
         )
-        self.split_silu_mul = torch.compile(GLUActivation(config.hidden_act))
+        self.split_silu_mul = GLUActivation(config.hidden_act)
 
     def forward(self, hidden_states):  # [seq_length, batch_size, hidden_dim]
         merged_states = self.gate_up_proj(hidden_states)
@@ -813,15 +813,12 @@ def forward_with_hidden_states(
 
     def get_block_compute_costs(self):
         """Computes the compute cost of each block in the model so that we can do a better job of load balancing."""
-        model_config = self.config
-        d_ff = model_config.intermediate_size
-        d_qkv = model_config.hidden_size // model_config.num_attention_heads
         block_compute_costs = {
             # CausalSelfAttention (qkv proj + attn out) + MLP
-            LlamaDecoderLayer: 4 * model_config.num_attention_heads * d_qkv * model_config.hidden_size
-            + 3 * d_ff * model_config.hidden_size,
+            Embedding: 1,
+            LlamaDecoderLayer: 1,
             # This is the last lm_head
-            TensorParallelColumnLinear: model_config.vocab_size * model_config.hidden_size,
+            TensorParallelColumnLinear: 1,
         }
         return block_compute_costs
 
diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index 70d023fb..bef629c1 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -700,7 +700,7 @@ def _load_model_checkpoint(self, model: NanotronModel) -> NanotronModel:
             )
             reloaded_from_checkpoint = True
         if not reloaded_from_checkpoint:
-            log_rank("No checkpoint path provided.", logger=logger, level=logging.INFO)
+            log_rank("No checkpoint path provided.", logger=logger, level=logging.INFO, rank=0)
             if isinstance(self.config.model.init_method, ExistingCheckpointInit):
                 # Initialize model from an pretrained model checkpoint
                 self.param_shard_metadata = load_weights(