diff --git a/vllm/worker/hpu_model_runner.py b/vllm/worker/hpu_model_runner.py
index d6eaf84e40f6b..4ac547ae326da 100644
--- a/vllm/worker/hpu_model_runner.py
+++ b/vllm/worker/hpu_model_runner.py
@@ -39,7 +39,10 @@ from vllm.lora.layers import LoRAMapping
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
 from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
 from vllm.model_executor.model_loader import get_model
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs)
@@ -311,10 +314,38 @@ class HpuModelAdapter:
         self.block_size = vllm_config.cache_config.block_size
         self.dtype = vllm_config.model_config.dtype
         enforce_eager = vllm_config.model_config.enforce_eager
+
         if not htorch.utils.internal.is_lazy() and not enforce_eager:
-            self.model = torch.compile(self.model,
-                                       backend='hpu_backend',
-                                       dynamic=False)
+            if os.getenv('VLLM_REGIONAL_COMPILATION',
+                         'true').lower() == 'true':
+                self.regional_compilation_layers_list = [
+                    RMSNorm, VocabParallelEmbedding
+                ]
+                self._regional_compilation(self.model)
+            else:
+                self.model = torch.compile(self.model,
+                                           backend='hpu_backend',
+                                           dynamic=False)
+
+    def _regional_compilation(self,
+                              module,
+                              parent_module=None,
+                              module_name=None):
+        if isinstance(module, torch.nn.ModuleList):
+            for children_name, children_module in module.named_children():
+                self._compile_region(module, children_name, children_module)
+        elif any(
+                isinstance(module, layer)
+                for layer in self.regional_compilation_layers_list):
+            self._compile_region(parent_module, module_name, module)
+        else:
+            for children_name, children_module in module.named_children():
+                self._regional_compilation(children_module, module,
+                                           children_name)
+
+    def _compile_region(self, model, name, module):
+        module = torch.compile(module, backend='hpu_backend', dynamic=False)
+        setattr(model, name, module)
 
     def _set_attn_bias(self, attn_metadata, batch_size, seq_len, device,
                        dtype):
@@ -1575,9 +1606,9 @@ class HPUModelRunnerBase(ModelRunnerBase[TModelInputForHPU]):
                     list(sorted(self.bucketing_global_state.decode_buckets)))
 
         if not htorch.utils.internal.is_lazy() and not self.enforce_eager:
-            cache_size_limit = len(
-                self.bucketing_global_state.prompt_buckets) + len(
-                    self.bucketing_global_state.decode_buckets) + 1
+            cache_size_limit = 1 + 3 * (
+                len(self.bucketing_global_state.prompt_buckets) +
+                len(self.bucketing_global_state.decode_buckets))
             torch._dynamo.config.cache_size_limit = max(
                 cache_size_limit, torch._dynamo.config.cache_size_limit)
             # Multiply by 8 to follow the original default ratio between