diff --git a/examples/configs/grpo_math_1B.yaml b/examples/configs/grpo_math_1B.yaml
index 253471de38..fe32b9691f 100644
--- a/examples/configs/grpo_math_1B.yaml
+++ b/examples/configs/grpo_math_1B.yaml
@@ -39,6 +39,7 @@ policy:
   precision: "bfloat16"
   fsdp_offload_enabled: false
   activation_checkpointing_enabled: false
+  refit_buffer_size_gb: 4 # used for refitting inference engine, the unit is GB
 
   dtensor_cfg:
     enabled: false
diff --git a/examples/configs/grpo_math_8B.yaml b/examples/configs/grpo_math_8B.yaml
index e791c66a34..dcd68a9509 100644
--- a/examples/configs/grpo_math_8B.yaml
+++ b/examples/configs/grpo_math_8B.yaml
@@ -17,6 +17,7 @@ policy:
   precision: "bfloat16"
   fsdp_offload_enabled: false
   activation_checkpointing_enabled: false
+  refit_buffer_size_gb: 4 # used for refitting inference engine, the unit is GB
 
   optimizer:
     name: "torch.optim.AdamW"
diff --git a/nemo_reinforcer/algorithms/grpo.py b/nemo_reinforcer/algorithms/grpo.py
index 155b49a8df..592a4bc4d8 100644
--- a/nemo_reinforcer/algorithms/grpo.py
+++ b/nemo_reinforcer/algorithms/grpo.py
@@ -279,13 +279,34 @@ def setup(
 def refit_policy_generation(
     policy: PolicyInterface,
     policy_generation: GenerationInterface,
+    refit_buffer_size_gb: int,  # GB
 ):
     """Refit the policy generation interface with the latest policy weights."""
     policy.offload_before_refit()
-    ipc_handles = policy.get_weights_ipc_handles()
-    policy_generation.prepare_for_generation()
-    policy_generation.update_weights(ipc_handles)
+    policy_generation.prepare_for_generation(tags=["weights"])
+    # Streaming update weights to save memory
+    state_dict_info = policy.prepare_weights_for_ipc()
+    # group keys to save time
+    available_bytes = refit_buffer_size_gb * (1024**3)
+    split_keys, keys = [], []
+    for key, size_in_bytes in state_dict_info:
+        if size_in_bytes > available_bytes:
+            if keys:
+                split_keys.append(keys)
+                keys = []
+            available_bytes = refit_buffer_size_gb * (1024**3)
+
+        keys.append(key)
+        available_bytes -= size_in_bytes
+
+    if len(keys) > 0:
+        split_keys.append(keys)
+    # do update
+    for keys in split_keys:
+        ipc_handles = policy.get_weights_ipc_handles(keys)
+        policy_generation.update_weights(ipc_handles)
     policy.offload_after_refit()
+    policy_generation.prepare_for_generation(tags=["kv_cache"])
 
 
 # ===============================================================================
@@ -321,12 +342,13 @@ def grpo_train(
     consumed_samples = grpo_save_state["consumed_samples"]
     val_period = master_config["grpo"]["val_period"]
     val_at_start = master_config["grpo"]["val_at_start"]
+    refit_buffer_size_gb = master_config["policy"]["refit_buffer_size_gb"]
 
     # Run validation at the start if configured
     if val_at_start and step == 0:
         print("\n🔍 Running initial validation...")
         if NEED_REFIT and POLICY_GENERATION_STALE:
-            refit_policy_generation(policy, policy_generation)
+            refit_policy_generation(policy, policy_generation, refit_buffer_size_gb)
             POLICY_GENERATION_STALE = False
         else:
             policy_generation.prepare_for_generation()
@@ -368,7 +390,11 @@ def grpo_train(
             print(f"▶ Generating responses for batch of size {repeated_batch.size}...")
             with timer.time("prepare_for_generation"):
                 if NEED_REFIT and POLICY_GENERATION_STALE:
-                    refit_policy_generation(policy, policy_generation)
+                    refit_policy_generation(
+                        policy,
+                        policy_generation,
+                        refit_buffer_size_gb,
+                    )
                     POLICY_GENERATION_STALE = False
                 else:
                     policy_generation.prepare_for_generation()
@@ -476,7 +502,11 @@ def grpo_train(
             # Run validation if it's a validation step
             if val_period > 0 and (step + 1) % val_period == 0:
                 if NEED_REFIT and POLICY_GENERATION_STALE:
-                    refit_policy_generation(policy, policy_generation)
+                    refit_policy_generation(
+                        policy,
+                        policy_generation,
+                        refit_buffer_size_gb,
+                    )
                     POLICY_GENERATION_STALE = False
                 else:
                     policy_generation.prepare_for_generation()
diff --git a/nemo_reinforcer/models/generation/vllm.py b/nemo_reinforcer/models/generation/vllm.py
index 03851f2de7..1f72eb3df0 100644
--- a/nemo_reinforcer/models/generation/vllm.py
+++ b/nemo_reinforcer/models/generation/vllm.py
@@ -454,8 +454,14 @@ def sleep(self):
         gc.collect()
         torch.cuda.empty_cache()
 
-    def wake_up(self):
-        self.llm.wake_up()
+    def wake_up(self, **kwargs):
+        # tags like ["weights", "kv_cache"]
+        # We can call this function with just tags=["weights"] while doing refit to
+        # avoid spiking memory with the kv_cache while the training fwk is awake.
+        if "tags" in kwargs:
+            self.llm.wake_up(tags=kwargs["tags"])
+        else:
+            self.llm.wake_up()
 
 
 class VllmGeneration(GenerationInterface):
@@ -622,7 +628,7 @@ def prepare_for_generation(self, *args, **kwargs):
         try:
             # Use run_all_workers_single_data for methods that don't need data
             futures = self.worker_group.run_all_workers_single_data(
-                "wake_up", only_on="tied_leader"
+                "wake_up", only_on="tied_leader", **kwargs
             )
             # Wait for all futures to complete
             results = ray.get(futures)
diff --git a/nemo_reinforcer/models/generation/vllm_backend.py b/nemo_reinforcer/models/generation/vllm_backend.py
index 1e5fa21a33..28cf9fbd2f 100644
--- a/nemo_reinforcer/models/generation/vllm_backend.py
+++ b/nemo_reinforcer/models/generation/vllm_backend.py
@@ -45,7 +45,7 @@ def update_weights_from_ipc_handles(self, ipc_handles):
             weights = []
 
             # Process each handle to get the tensor
-            for name, handle in handles.items():
+            for name, handle in handles:
                 func, args = handle
                 list_args = list(args)
                 # Update device ID to match the current device
diff --git a/nemo_reinforcer/models/policy/__init__.py b/nemo_reinforcer/models/policy/__init__.py
index 795e08f895..c83a8d0bf9 100644
--- a/nemo_reinforcer/models/policy/__init__.py
+++ b/nemo_reinforcer/models/policy/__init__.py
@@ -44,3 +44,4 @@ class PolicyConfig(TypedDict):
     max_grad_norm: Optional[Union[float, int]]
     fsdp_offload_enabled: bool
     activation_checkpointing_enabled: bool
+    refit_buffer_size_gb: int
diff --git a/nemo_reinforcer/models/policy/dtensor_policy_worker.py b/nemo_reinforcer/models/policy/dtensor_policy_worker.py
index 2c4bd78efd..852d0d20d5 100644
--- a/nemo_reinforcer/models/policy/dtensor_policy_worker.py
+++ b/nemo_reinforcer/models/policy/dtensor_policy_worker.py
@@ -174,7 +174,9 @@ def __init__(
         if self.cpu_offload:
             self.model = self.move_buffer_to_device(self.model, "cpu")
 
-        self._held_model_params = None
+        # used for streaming update inference engine weights
+        self._held_sharded_state_dict_reference = None
+        self._held_streamed_param_reference = None
 
         if init_reference_model:
             self.reference_model_state_dict = get_cpu_state_dict(
@@ -235,6 +237,9 @@ def __init__(
     def is_alive(self):
         return True
 
+    def reset_peak_memory_stats(self):
+        torch.cuda.reset_peak_memory_stats()
+
     def get_gpu_info(self):
         """Return information about the GPU being used by this worker."""
         return get_gpu_info(self.model)
@@ -554,50 +559,45 @@ def report_device_id(self) -> str:
         return get_device_uuid(device_idx)
 
     @torch.no_grad()
-    def get_weight_ipc_handles(self, offload_model=True):
-        from torch.multiprocessing.reductions import reduce_tensor
-
+    def prepare_weights_for_ipc(self):
         self.model = self.move_to_cuda(self.model)
-        params = self.model.state_dict()
+        self._held_sharded_state_dict_reference = self.model.state_dict()
+        # Collect info for streaming multiple tensors
+        state_dict_info = []
+        for name, tensor in self._held_sharded_state_dict_reference.items():
+            # dtensor's numel will return complete tensor instead of only local tensor
+            size_in_bytes = tensor.element_size() * tensor.numel()
+            state_dict_info.append((name, size_in_bytes))
+        return state_dict_info
 
-        # Create a copy of parameters in the desired dtype (bfloat16 or float32)
-        dtype_params = {}
-        for name, param in params.items():
-            if isinstance(param, DTensor):
-                param = param.full_tensor()
+    @torch.no_grad()
+    def get_weights_ipc_handles(self, keys):
+        from torch.multiprocessing.reductions import reduce_tensor
 
+        converted_params = {}
+        for key in keys:
+            # Get full_tensor for dtensor (GPU > 1)
+            tensor = self._held_sharded_state_dict_reference[key]
+            if isinstance(tensor, DTensor):
+                full_tensor = tensor.full_tensor()
+            else:
+                full_tensor = tensor
             # Convert parameters to the configured dtype
-            dtype_params[name] = param.to(
-                device="cuda", dtype=self.dtype, non_blocking=True
-            )
+            converted_params[key] = full_tensor.to(self.dtype, non_blocking=True)
 
-        for name, buffer in self.model.named_buffers():
-            if isinstance(buffer, DTensor):
-                buffer = buffer.full_tensor()
+        # Temporary record the full tensor for cleanup
+        # It is needed for cleanup the last full_tensor in the refit process
+        self._held_streamed_param_reference = converted_params
 
-            dtype_params[name] = buffer.to(
-                device="cuda", dtype=self.dtype, non_blocking=True
-            )
-
-        torch.cuda.synchronize()
-
-        # Replace the original params with the converted ones
-        params = dtype_params
-
-        # hold on to the params so we can explicitly delete them after refit
-        self._held_model_params = params
-
-        data = {}
+        # Get device UUID for IPC
         device_uuid = self.report_device_id()
-        for name, p in params.items():
-            data[name] = reduce_tensor(p.detach())
-
-        if offload_model or self.cpu_offload:
-            self.model = self.move_to_cpu(self.model)
-            gc.collect()
-            torch.cuda.empty_cache()
+        # Create handles for the tensors
+        all_handles = []
+        for key, p in converted_params.items():
+            handle = reduce_tensor(p.detach())
+            all_handles.append((key, handle))
 
-        return {device_uuid: data}
+        return {device_uuid: all_handles}
 
     def prepare_for_lp_inference(self):
         if not self.cpu_offload:
@@ -655,9 +655,13 @@ def offload_after_refit(self):
         torch.randn(1).cuda()  # wake up torch allocator
         self.offload_before_refit()  # rerun the old offload function
 
-        if self._held_model_params is not None:
-            del self._held_model_params
-            self._held_model_params = None
+        # Clean up the held tensors
+        if self._held_sharded_state_dict_reference is not None:
+            del self._held_sharded_state_dict_reference
+            self._held_sharded_state_dict_reference = None
+        if self._held_streamed_param_reference is not None:
+            del self._held_streamed_param_reference
+            self._held_streamed_param_reference = None
 
         gc.collect()
         torch.cuda.empty_cache()
diff --git a/nemo_reinforcer/models/policy/fsdp1_policy_worker.py b/nemo_reinforcer/models/policy/fsdp1_policy_worker.py
index 3ff7faed70..566d68b87f 100644
--- a/nemo_reinforcer/models/policy/fsdp1_policy_worker.py
+++ b/nemo_reinforcer/models/policy/fsdp1_policy_worker.py
@@ -149,7 +149,11 @@ def do_fsdp(model):
             self.reference_model = do_fsdp(self.reference_model)
             self.reference_model = self.manual_offload_to_cpu(self.reference_model)
         self.model = self.manual_load_to_gpu(self.model)
-        self._held_reference_model_params = None
+
+        # used for streaming update inference engine weights
+        self._held_sharded_state_dict_reference = None
+        self._held_streamed_param_reference = None
+
         # register_fsdp_forward_method(self.model, "generate")
         if init_optimizer:
             optimizer_cls = import_class_from_path(self.cfg["optimizer"]["name"])
@@ -205,6 +209,9 @@ def do_fsdp(model):
     def is_alive(self):
         return True
 
+    def reset_peak_memory_stats(self):
+        torch.cuda.reset_peak_memory_stats()
+
     def get_gpu_info(self):
         """Return information about the GPU being used by this worker."""
         return get_gpu_info(self.model)
@@ -720,38 +727,61 @@ def report_device_id(self) -> str:
         return get_device_uuid(device_idx)
 
     @torch.no_grad()
-    def get_weight_ipc_handles(self, offload_model=True):
-        from torch.multiprocessing.reductions import reduce_tensor
+    def prepare_weights_for_ipc(self):
+        from torch.distributed.fsdp.api import ShardedStateDictConfig, StateDictType
 
         # If the model is not FSDP, then we need to manually move it to the GPU
         # For an FSDP model, model.state_dict() will move the params to the GPU
-        if not isinstance(self.model, torch.distributed.fsdp.FullyShardedDataParallel):
+        if not isinstance(self.model, FullyShardedDataParallel):
             self.model = self.manual_load_to_gpu(self.model)
+            self._held_sharded_state_dict_reference = self.model.state_dict()
+        else:
+            # Get sharded state dict instead of full state dict for FSDP1
+            with FullyShardedDataParallel.state_dict_type(
+                self.model,
+                state_dict_type=StateDictType.SHARDED_STATE_DICT,
+                state_dict_config=ShardedStateDictConfig(),
+            ):
+                self._held_sharded_state_dict_reference = self.model.state_dict()
+
+        # Collect info for streaming multiple tensors
+        state_dict_info = []
+        for name, tensor in self._held_sharded_state_dict_reference.items():
+            # dtensor's numel will return complete tensor instead of only local tensor
+            size_in_bytes = tensor.element_size() * tensor.numel()
+            state_dict_info.append((name, size_in_bytes))
+
+        return state_dict_info
 
-        # TODO @sahilj: do this without an allgather (maybe FSDP2)
-        params = self.model.state_dict()
+    @torch.no_grad()
+    def get_weights_ipc_handles(self, keys):
+        from torch.distributed.tensor import DTensor
+        from torch.multiprocessing.reductions import reduce_tensor
 
-        # Create a copy of parameters in the desired dtype (bfloat16 or float32)
-        dtype_params = {}
-        for name, param in params.items():
+        converted_params = {}
+        for key in keys:
+            # Get full_tensor for dtensor (GPU > 1)
+            tensor = self._held_sharded_state_dict_reference[key]
+            if isinstance(tensor, DTensor):
+                full_tensor = tensor.full_tensor()
+            else:
+                full_tensor = tensor
             # Convert parameters to the configured dtype
-            dtype_params[name] = param.to(self.dtype, non_blocking=True)
-
-        # Replace the original params with the converted ones
-        params = dtype_params
-        # For FSDP1, params may get GC'ed before sending to vllm,
-        # so we need to hold a reference to them
-        self._held_reference_model_params = params
-        data = {}
+            converted_params[key] = full_tensor.to(self.dtype, non_blocking=True)
+
+        # Temporary record the full tensor for cleanup
+        # It is needed for cleanup the last full_tensor in the refit process
+        self._held_streamed_param_reference = converted_params
+
+        # Get device UUID for IPC
         device_uuid = self.report_device_id()
-        for name, p in params.items():
-            data[name] = reduce_tensor(p.detach())
+        # Create handles for the tensors
+        all_handles = []
+        for key, p in converted_params.items():
+            handle = reduce_tensor(p.detach())
+            all_handles.append((key, handle))
 
-        if offload_model:
-            self.model = self.manual_offload_to_cpu(self.model)
-            gc.collect()
-            torch.cuda.empty_cache()
-        return {device_uuid: data}
+        return {device_uuid: all_handles}
 
     def prepare_for_lp_inference(self):
         self.model = self.manual_load_to_gpu(self.model)
@@ -802,9 +832,13 @@ def offload_after_refit(self):
         torch.randn(1).cuda()  # wake up torch allocator
         self.offload_before_refit()  # rerun the old offload function
 
-        if self._held_reference_model_params is not None:
-            del self._held_reference_model_params
-            self._held_reference_model_params = None
+        # Clean up the held tensors
+        if self._held_sharded_state_dict_reference is not None:
+            del self._held_sharded_state_dict_reference
+            self._held_sharded_state_dict_reference = None
+        if self._held_streamed_param_reference is not None:
+            del self._held_streamed_param_reference
+            self._held_streamed_param_reference = None
 
         gc.collect()
         torch.cuda.empty_cache()
diff --git a/nemo_reinforcer/models/policy/hf_policy.py b/nemo_reinforcer/models/policy/hf_policy.py
index a068c27794..c4b0718288 100644
--- a/nemo_reinforcer/models/policy/hf_policy.py
+++ b/nemo_reinforcer/models/policy/hf_policy.py
@@ -253,7 +253,19 @@ def finish_training(self, *args, **kwargs):
         # Placeholder implementation
         pass
 
-    def get_weights_ipc_handles(self):
+    def prepare_weights_for_ipc(self):
+        """Prepare the weights for IPC.
+
+        Returns:
+            dict: A dictionary containing the state_dict_info of the model.
+        """
+        futures = self.worker_group.run_all_workers_single_data(
+            "prepare_weights_for_ipc", only_on="all_tied_workers"
+        )
+        # only get the first worker's result is enough since all workers will have the same result
+        return ray.get(futures)[0]
+
+    def get_weights_ipc_handles(self, key):
         """Fetch weight IPC handles from all workers.
 
         Returns:
@@ -262,7 +274,7 @@ def get_weights_ipc_handles(self):
         # Collect IPC handles from all workers
         worker_handles = ray.get(
             [
-                worker.get_weight_ipc_handles.remote()
+                worker.get_weights_ipc_handles.remote(key)
                 for worker in self.worker_group.workers
             ]
         )
diff --git a/nemo_reinforcer/models/policy/utils.py b/nemo_reinforcer/models/policy/utils.py
index 0c249a7860..af2e84245e 100644
--- a/nemo_reinforcer/models/policy/utils.py
+++ b/nemo_reinforcer/models/policy/utils.py
@@ -45,6 +45,8 @@ def get_gpu_info(model):
     device_count = torch.cuda.device_count()
     memory_allocated = torch.cuda.memory_allocated(device) / (1024**2)  # in MB
     memory_reserved = torch.cuda.memory_reserved(device) / (1024**2)  # in MB
+    peak_memory = torch.cuda.max_memory_allocated() / (1024**2)  # in MB
+    peak_reserved = torch.cuda.max_memory_reserved() / (1024**2)  # in MB
 
     # Try to get the real global device ID (not the local one)
     # In distributed training, each process only sees its assigned GPU as device 0
@@ -83,6 +85,8 @@ def get_gpu_info(model):
         "device_name": device_name,
         "memory_allocated_mb": memory_allocated,
         "memory_reserved_mb": memory_reserved,
+        "peak_memory_allocated_mb": peak_memory,
+        "peak_memory_reserved_mb": peak_reserved,
         "parameter_sample": param_info,
         "env_vars": {
             k: v
diff --git a/pyproject.toml b/pyproject.toml
index b7f8260ff8..83c8f86ab7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,7 @@ readme = {file = "README.md", content-type = "text/markdown"}
 
 [project.optional-dependencies]
 vllm = [
-    "vllm==0.8.2",
+    "vllm==0.8.4",
 ]
 
 [dependency-groups]
diff --git a/tests/unit/models/generation/test_vllm_generation.py b/tests/unit/models/generation/test_vllm_generation.py
index 3f361394a9..2232ea6499 100644
--- a/tests/unit/models/generation/test_vllm_generation.py
+++ b/tests/unit/models/generation/test_vllm_generation.py
@@ -18,6 +18,7 @@
 import torch
 import ray
 
+from nemo_reinforcer.algorithms.grpo import refit_policy_generation
 from nemo_reinforcer.algorithms.utils import get_tokenizer
 from nemo_reinforcer.distributed.virtual_cluster import RayVirtualCluster
 from nemo_reinforcer.distributed.batched_data_dict import BatchedDataDict
@@ -65,6 +66,7 @@ def get_basic_hf_test_config(enable_dtensor: bool = False) -> PolicyConfig:
         "precision": "float32",
         "fsdp_offload_enabled": False,
         "activation_checkpointing_enabled": False,
+        "refit_buffer_size_gb": 4,
         "optimizer": {
             "name": "torch.optim.AdamW",
             "kwargs": {
@@ -270,9 +272,7 @@ def test_vllm_worker_seed_behavior(cluster, tokenizer):
     hf_policy = HfPolicy(cluster, hf_config, tokenizer)
 
     print(f"refitting vllm policy...")
-    ipc_handles = hf_policy.get_weights_ipc_handles()
-    policy.prepare_for_generation()
-    policy.update_weights(ipc_handles)
+    refit_policy_generation(hf_policy, policy, hf_config["refit_buffer_size_gb"])
 
     try:
         # Generate with duplicated prompts
@@ -435,9 +435,9 @@ def test_vllm_generation_with_hf_training(cluster, tokenizer, enable_dtensor):
         hf_policy = HfPolicy(cluster, hf_config, tokenizer)
 
         print(f"refitting vllm policy...")
-        ipc_handles = hf_policy.get_weights_ipc_handles()
-        vllm_policy.prepare_for_generation()
-        vllm_policy.update_weights(ipc_handles)
+        refit_policy_generation(
+            hf_policy, vllm_policy, hf_config["refit_buffer_size_gb"]
+        )
 
         # Step 1: Use vLLM for generation
         print("Using vLLM policy for fast generation...")
@@ -709,9 +709,11 @@ def test_vllm_weight_update_and_prefix_cache_reset(
         )
 
         print("Updating vLLM weights from HF policy...")
-        ipc_handles = hf_policy.get_weights_ipc_handles()
-        update_success = vllm_policy.update_weights(ipc_handles)
-        assert update_success, "Weight update should succeed"
+        param_keys = hf_policy.prepare_weights_for_ipc()
+        for key, _ in param_keys:
+            ipc_handles = hf_policy.get_weights_ipc_handles([key])
+            update_success = vllm_policy.update_weights(ipc_handles)
+            assert update_success, "Weight update should succeed"
         print("vLLM weights successfully updated.")
 
         print("Running Generation 2 (Weights Updated, Cache Still Active)...")
@@ -749,6 +751,69 @@ def test_vllm_weight_update_and_prefix_cache_reset(
         torch.cuda.empty_cache()
 
 
+@pytest.mark.parametrize("enable_dtensor", [True, False])
+def test_vllm_weight_update_memory(cluster, tokenizer, enable_dtensor):
+    """Test that vLLM streaming weight update and can save memory."""
+    from nemo_reinforcer.models.policy.hf_policy import HfPolicy
+
+    if cluster.num_gpus_per_node < 2:
+        pytest.skip("Need at least 2 GPUs per node for this test")
+
+    # Create separate configs for each policy
+    vllm_config = basic_vllm_test_config.copy()
+    vllm_config = configure_generation_config(vllm_config, tokenizer, is_eval=False)
+
+    # Ensure we can get same peak memory
+    assert vllm_config["model_name"] == "meta-llama/Llama-3.2-1B", (
+        "Model name should be meta-llama/Llama-3.2-1B to get expected peak memory"
+    )
+
+    # Create policies
+    print("Creating vLLM policy...")
+    vllm_policy = VllmGeneration(cluster, vllm_config)
+    vllm_policy.finish_generation()
+
+    print("Creating HF policy...")
+    hf_config = get_basic_hf_test_config(enable_dtensor=enable_dtensor)
+    hf_policy = HfPolicy(cluster, hf_config, tokenizer)
+
+    print(f"refitting vllm policy...")
+    # take it outside statistics to get clean peak memory during refit
+    hf_policy.offload_before_refit()
+    # reset peak memory stats before refit
+    workers = hf_policy.worker_group.workers
+    ray.get([w.reset_peak_memory_stats.remote() for w in workers])
+    refit_policy_generation(hf_policy, vllm_policy, refit_buffer_size_gb=1)
+    gpu_infos = ray.get([w.get_gpu_info.remote() for w in workers])
+
+    # Gather memory stats
+    current_allocated = 0.0
+    current_reserved = 0.0
+    peak_allocated = 0.0
+    peak_reserved = 0.0
+    for status in gpu_infos:
+        current_allocated = max(current_allocated, status["memory_allocated_mb"])
+        current_reserved = max(current_reserved, status["memory_reserved_mb"])
+        peak_allocated = max(peak_allocated, status["peak_memory_allocated_mb"])
+        peak_reserved = max(peak_reserved, status["peak_memory_reserved_mb"])
+
+    # Check memory stats
+    assert current_allocated == 0.0, "Memory should be 0 after refit completed"
+    assert current_reserved == 0.0, "Memory should be 0 after refit completed"
+    # memory threshold: memory during non-streaming weight update on 1B model on 2 GPUs
+    # memory during streaming weight update should less than this baseline threshold
+    if enable_dtensor:
+        assert peak_allocated < 8074, "Peak allocated memory should < 8074 MB"
+        assert peak_reserved < 8088, "Peak reserved memory should < 8088 MB"
+    else:
+        assert peak_allocated < 11286, "Peak allocated memory should < 11286 MB"
+        assert peak_reserved < 11298, "Peak reserved memory should < 11298 MB"
+
+    # Clean up
+    vllm_policy.shutdown()
+    hf_policy.shutdown()
+
+
 @pytest.mark.parametrize("is_eval", [True, False])
 @pytest.mark.parametrize("enable_dtensor", [True, False])
 def test_vllm_generation_with_stop(
@@ -785,9 +850,11 @@ def test_vllm_generation_with_stop(
         hf_policy = HfPolicy(cluster, hf_config, tokenizer)
 
         print(f"refitting vllm policy...")
-        ipc_handles = hf_policy.get_weights_ipc_handles()
-        vllm_generation.prepare_for_generation()
-        vllm_generation.update_weights(ipc_handles)
+        refit_policy_generation(
+            hf_policy,
+            vllm_generation,
+            hf_config["refit_buffer_size_gb"],
+        )
 
     # test generate
     outputs = vllm_generation.generate(test_input_data, greedy=True)
diff --git a/uv.lock b/uv.lock
index d546f25e64..9902633207 100644
--- a/uv.lock
+++ b/uv.lock
@@ -549,16 +549,16 @@ wheels = [
 
 [[package]]
 name = "compressed-tensors"
-version = "0.9.2"
+version = "0.9.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
     { name = "torch" },
     { name = "transformers" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/91/3e/f74c5dcca6552e15a00df4a78c6e4a8776a7c901acc5a8c1dd371698ef54/compressed_tensors-0.9.3.tar.gz", hash = "sha256:5bdc7774a6c217496cba7d6a4fca6ffac943e68adae0481ead6d036660c1b340", size = 66354 }
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+    { url = "https://files.pythonhosted.org/packages/79/87/9c7eb4b57f89a51a65bee166cc079cd1bc1b398823da4f3b3c12f1021af8/compressed_tensors-0.9.3-py3-none-any.whl", hash = "sha256:5fcc3e4e7aa828036c2aeb130a610f9745a2e4890692cad6f6b5a2f960b21cc1", size = 98449 },
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 ]
 
+[[package]]
+name = "deprecated"
+version = "1.2.18"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/98/97/06afe62762c9a8a86af0cfb7bfdab22a43ad17138b07af5b1a58442690a2/deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d", size = 2928744 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6e/c6/ac0b6c1e2d138f1002bcf799d330bd6d85084fece321e662a14223794041/Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec", size = 9998 },
+]
+
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 name = "depyf"
 version = "0.18.0"
@@ -953,16 +965,17 @@ http = [
 
 [[package]]
 name = "gguf"
-version = "0.10.0"
+version = "0.16.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "pyyaml" },
+    { name = "sentencepiece" },
     { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0e/c4/a159e9f842b0e8b8495b2689af6cf3426f002cf01207ca8134db82fc4088/gguf-0.10.0.tar.gz", hash = "sha256:52a30ef26328b419ffc47d9269fc580c238edf1c8a19b5ea143c323e04a038c1", size = 65704 }
+sdist = { url = "https://files.pythonhosted.org/packages/c8/56/9c34a40ef5ad96e02cfe49958cf884496f145d101605551663753ae1657c/gguf-0.16.2.tar.gz", hash = "sha256:0fc956289a30d0f1f3afd75ec0d493f73ae2629a3f21f3846dd1687d8791c7c1", size = 85129 }
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+[[package]]
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+version = "1.0.3"
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+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/4d/a2/d7a5f452a3a8faaa82aeb3aceddab2e103c1b7028a00bbc4caebca5d79fe/hf_xet-1.0.3-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:5927d1986f87b7b80616eb6353a1402be1d72c46b6b0709b01ffc7623a159563", size = 53739471 },
+    { url = "https://files.pythonhosted.org/packages/82/81/966f800933043c0be989306f5224ef058543f7848f1e78d7ef3305bd069a/hf_xet-1.0.3-cp37-abi3-win_amd64.whl", hash = "sha256:014b5a40e62ad334f21513e5ba39b419117396031e9264dfc15dd598a1595029", size = 4123538 },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.7"
@@ -1154,7 +1182,7 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.29.3"
+version = "0.30.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "filelock" },
@@ -1165,9 +1193,14 @@ dependencies = [
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 ]
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+    { url = "https://files.pythonhosted.org/packages/93/27/1fb384a841e9661faad1c31cbfa62864f59632e876df5d795234da51c395/huggingface_hub-0.30.2-py3-none-any.whl", hash = "sha256:68ff05969927058cfa41df4f2155d4bb48f5f54f719dd0390103eefa9b191e28", size = 481433 },
+]
+
+[package.optional-dependencies]
+hf-xet = [
+    { name = "hf-xet" },
 ]
 
 [[package]]
@@ -1213,14 +1246,14 @@ wheels = [
 
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