diff --git a/optimum/habana/accelerate/utils/transformer_engine.py b/optimum/habana/accelerate/utils/transformer_engine.py
index df0a07b5c4..89e1a895aa 100755
--- a/optimum/habana/accelerate/utils/transformer_engine.py
+++ b/optimum/habana/accelerate/utils/transformer_engine.py
@@ -16,6 +16,13 @@
 import functools
 
 import torch
+from transformers.utils import (
+    is_peft_available,
+)
+
+
+if is_peft_available():
+    from peft.tuners import lora
 
 
 has_transformer_engine = False
@@ -47,7 +54,30 @@ def _convert_model(model, to_transformer_engine=True, _convert_linear=True, _min
     if not is_fp8_available():
         raise ImportError("Using `convert_model` requires transformer_engine to be installed.")
     for name, module in model.named_children():
-        if isinstance(module, torch.nn.Linear) and to_transformer_engine and _convert_linear:
+        if is_peft_available() and isinstance(module, lora.Linear) and to_transformer_engine and _convert_linear:
+            # For lora linear module, convert only base linear layer to fp8 and skip lora-a,
+            # lora-b linear layers. Since lora-a, lora-b are small in size, there is not much
+            # device performance gain by pushing these in fp8. This way we avoid host overhead
+            # associated with using TE for these layers.
+            for name, lora_module in module.named_children():
+                if name == "base_layer":
+                    has_bias = lora_module.bias is not None
+                    # Initializing TE linear without weights and biases and shallow copying them from the original module.
+                    te_module = te.Linear(
+                        lora_module.in_features,
+                        lora_module.out_features,
+                        bias=has_bias,
+                        params_dtype=lora_module.weight.dtype,
+                        skip_weight_param_allocation=True,
+                        minimize_memory=_minimize_memory,
+                    )
+                    te_module.weight = lora_module.weight
+
+                    if has_bias:
+                        te_module.bias = lora_module.bias
+
+                    setattr(module, name, te_module)
+        elif isinstance(module, torch.nn.Linear) and to_transformer_engine and _convert_linear:
             has_bias = module.bias is not None
             # Initializing TE linear without weights and biases and shallow copying them from the original module.
             te_module = te.Linear(