diff --git a/torchtrain/parallelisms/parallelize_llama.py b/torchtrain/parallelisms/parallelize_llama.py
index 38014e53b..9067ae85a 100644
--- a/torchtrain/parallelisms/parallelize_llama.py
+++ b/torchtrain/parallelisms/parallelize_llama.py
@@ -5,6 +5,7 @@
 # llama model, i.e. activation checkpointing, etc.
 
 from collections import defaultdict
+from typing import Tuple
 
 import torch
 from torch.distributed._tensor import Replicate, Shard
@@ -116,6 +117,23 @@ def selective_checkpointing_context_fn():
         )
 
 
+def get_tp_parallel_strategy(
+    job_config: JobConfig,
+) -> Tuple[RowwiseParallel, ColwiseParallel]:
+    """Get the parallel strategy for the transformer model.
+
+    This function handles the special case of using float8 with tensor parallelism.
+    """
+    if job_config.training.fp8_linear == "dynamic":
+        from float8_experimental.float8_tensor_parallel import (
+            Float8ColwiseParallel,
+            Float8RowwiseParallel,
+        )
+
+        return Float8RowwiseParallel, Float8ColwiseParallel
+    return RowwiseParallel, ColwiseParallel
+
+
 def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
     """
     Apply parallelisms to the model, including PTD parallelisms, and AC.
@@ -132,6 +150,10 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
         tp_mesh = world_mesh["sp"]
         sp_degree = job_config.training.sequence_parallel_degree
 
+        row_parallel_strategy, col_parallel_strategy = get_tp_parallel_strategy(
+            job_config
+        )
+
         # First:
         # 1. parallelize the first embedding and the last linear proj layer
         # 2. shard the first layer of transformer block
@@ -142,7 +164,7 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
                 "embeddings.tok_embeddings": RowwiseParallel(
                     input_layouts=Replicate(),
                 ),
-                "output": ColwiseParallel(
+                "output": col_parallel_strategy(
                     input_layouts=Shard(0),
                     output_layouts=Shard(-1)
                     if parallel_dims.loss_parallel_enabled
@@ -165,18 +187,18 @@ def parallelize_llama(model, world_mesh, parallel_dims, job_config: JobConfig):
                     input_layouts=(Shard(0), None),
                     desired_input_layouts=(Replicate(), None),
                 ),
-                "attention.wq": ColwiseParallel(),
-                "attention.wk": ColwiseParallel(),
-                "attention.wv": ColwiseParallel(),
-                "attention.wo": RowwiseParallel(output_layouts=Shard(0)),
+                "attention.wq": col_parallel_strategy(),
+                "attention.wk": col_parallel_strategy(),
+                "attention.wv": col_parallel_strategy(),
+                "attention.wo": row_parallel_strategy(output_layouts=Shard(0)),
                 "attention_norm": SequenceParallel(sequence_dim=0),
                 "feed_forward": PrepareModuleInput(
                     input_layouts=(Shard(0),),
                     desired_input_layouts=(Replicate(),),
                 ),
-                "feed_forward.w1": ColwiseParallel(),
-                "feed_forward.w2": RowwiseParallel(output_layouts=Shard(0)),
-                "feed_forward.w3": ColwiseParallel(),
+                "feed_forward.w1": col_parallel_strategy(),
+                "feed_forward.w2": row_parallel_strategy(output_layouts=Shard(0)),
+                "feed_forward.w3": col_parallel_strategy(),
                 "ffn_norm": SequenceParallel(sequence_dim=0),
             }