Support loading weights from remote instance (#8215)

Signed-off-by: Anqi Shen <amy.saq@antgroup.com>
Co-authored-by: Chayenne <74843776+zhaochenyang20@users.noreply.github.com>

python/sglang/srt/configs/device_config.py

@@ -8,10 +8,12 @@ logger = logging.getLogger(__name__)
 
 class DeviceConfig:
     device: Optional[torch.device]
+    gpu_id: Optional[int]
 
-    def __init__(self, device: str = "cuda") -> None:
+    def __init__(self, device: str = "cuda", gpu_id: int = -1) -> None:
         if device in ["cuda", "xpu", "hpu", "cpu", "npu"]:
             self.device_type = device
         else:
             raise RuntimeError(f"Not supported device type: {device}")
         self.device = torch.device(self.device_type)
+        self.gpu_id = gpu_id

python/sglang/srt/configs/load_config.py

@@ -23,6 +23,7 @@ class LoadFormat(str, enum.Enum):
     LAYERED = "layered"
     JAX = "jax"
     REMOTE = "remote"
+    REMOTE_INSTANCE = "remote_instance"
 
 
 @dataclass

python/sglang/srt/configs/model_config.py

@@ -64,12 +64,28 @@ class ModelConfig:
         is_draft_model: bool = False,
         hybrid_kvcache_ratio: Optional[float] = None,
         model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
+        tp_rank: Optional[int] = None,
+        remote_instance_weight_loader_seed_instance_ip: Optional[str] = None,
+        remote_instance_weight_loader_seed_instance_service_port: Optional[int] = None,
+        remote_instance_weight_loader_send_weights_group_ports: Optional[
+            List[int]
+        ] = None,
     ) -> None:
         # Parse args
         self.model_path = model_path
         self.revision = revision
         self.quantization = quantization
         self.model_impl = model_impl
+        self.tp_rank = tp_rank
+        self.remote_instance_weight_loader_seed_instance_ip = (
+            remote_instance_weight_loader_seed_instance_ip
+        )
+        self.remote_instance_weight_loader_seed_instance_service_port = (
+            remote_instance_weight_loader_seed_instance_service_port
+        )
+        self.remote_instance_weight_loader_send_weights_group_ports = (
+            remote_instance_weight_loader_send_weights_group_ports
+        )
 
         self.maybe_pull_model_tokenizer_from_remote()
         self.model_override_args = json.loads(model_override_args)
@@ -329,6 +345,9 @@ class ModelConfig:
             quantization=server_args.quantization,
             hybrid_kvcache_ratio=server_args.hybrid_kvcache_ratio,
             model_impl=server_args.model_impl,
+            remote_instance_weight_loader_seed_instance_ip=server_args.remote_instance_weight_loader_seed_instance_ip,
+            remote_instance_weight_loader_seed_instance_service_port=server_args.remote_instance_weight_loader_seed_instance_service_port,
+            remote_instance_weight_loader_send_weights_group_ports=server_args.remote_instance_weight_loader_send_weights_group_ports,
             **kwargs,
         )
 

python/sglang/srt/connector/__init__.py

@@ -9,6 +9,7 @@ from sglang.srt.connector.base_connector import (
     BaseKVConnector,
 )
 from sglang.srt.connector.redis import RedisConnector
+from sglang.srt.connector.remote_instance import RemoteInstanceConnector
 from sglang.srt.connector.s3 import S3Connector
 from sglang.srt.utils import parse_connector_type
 
@@ -18,14 +19,17 @@ logger = logging.getLogger(__name__)
 class ConnectorType(str, enum.Enum):
     FS = "filesystem"
     KV = "KV"
+    INSTANCE = "instance"
 
 
-def create_remote_connector(url, **kwargs) -> BaseConnector:
+def create_remote_connector(url, device, **kwargs) -> BaseConnector:
     connector_type = parse_connector_type(url)
     if connector_type == "redis":
         return RedisConnector(url)
     elif connector_type == "s3":
         return S3Connector(url)
+    elif connector_type == "instance":
+        return RemoteInstanceConnector(url, device)
     else:
         raise ValueError(f"Invalid connector type: {url}")
 
@@ -35,6 +39,8 @@ def get_connector_type(client: BaseConnector) -> ConnectorType:
         return ConnectorType.KV
     if isinstance(client, BaseFileConnector):
         return ConnectorType.FS
+    if isinstance(client, RemoteInstanceConnector):
+        return ConnectorType.INSTANCE
 
     raise ValueError(f"Invalid connector type: {client}")
 
@@ -44,6 +50,7 @@ __all__ = [
     "BaseFileConnector",
     "BaseKVConnector",
     "RedisConnector",
+    "RemoteInstanceConnector",
     "S3Connector",
     "ConnectorType",
     "create_remote_connector",

python/sglang/srt/connector/remote_instance.py

@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from typing import Generator, List, Optional, Tuple
+from urllib.parse import urlparse
+
+import torch
+import torch.distributed as dist
+
+from sglang.srt.connector import BaseConnector
+from sglang.srt.utils import init_custom_process_group
+
+logger = logging.getLogger(__name__)
+
+
+class RemoteInstanceConnector(BaseConnector):
+
+    def __init__(self, url: str, device: torch.device = "cpu"):
+        assert (
+            device.type == "cuda"
+        ), "RemoteInstanceConnector only supports cuda device."
+        super().__init__(url)
+        self.url = url
+        self.device = device
+
+    def build_group(
+        self,
+        gpu_id: int = -1,
+        tp_rank: int = -1,
+        instance_ip: str = None,
+        group_rank: int = 1,
+        world_size: int = 2,
+    ):
+        assert (
+            self.device.type == "cuda"
+        ), "RemoteInstanceConnector only supports cuda device."
+        assert (
+            gpu_id != -1 and tp_rank != -1
+        ), "gpu_id and tp_rank must be specified for RemoteInstanceConnector. "
+
+        self.device_id = torch.device(self.device.type, gpu_id)
+
+        parsed_url = urlparse(self.url)
+        master_address = parsed_url.hostname
+        master_port = parsed_url.port
+        group_name = f"send_weights_{instance_ip}_{master_port}_{tp_rank}"
+        backend = "nccl"
+
+        logger.info(
+            f"init custom process group: master_address={master_address}, master_port={master_port}, "
+            f"rank_offset={group_rank}, world_size={world_size}, group_name={group_name}, backend={backend}"
+        )
+
+        try:
+            self._model_update_group = init_custom_process_group(
+                backend=backend,
+                init_method=f"tcp://{master_address}:{master_port}",
+                world_size=world_size,
+                rank=group_rank,
+                group_name=group_name,
+                device_id=self.device_id,
+            )
+            dist.barrier(group=self._model_update_group)
+            return True, "Succeeded to initialize custom process group."
+        except Exception as e:
+            message = f"Failed to initialize custom process group: {e}."
+            logger.error(message)
+            return False, message
+
+    # Implemented as a no-op to make BaseConnector interface consistent.
+    def pull_files(
+        self,
+        allow_pattern: Optional[list[str]] = None,
+        ignore_pattern: Optional[list[str]] = None,
+    ) -> None:
+        return
+
+    # Implemented as a no-op to make BaseConnector interface consistent.
+    def weight_iterator(
+        self, rank: int = 0
+    ) -> Generator[Tuple[str, torch.Tensor], None, None]:
+        return

python/sglang/srt/entrypoints/http_server.py

@@ -73,6 +73,7 @@ from sglang.srt.managers.io_struct import (
     EmbeddingReqInput,
     GenerateReqInput,
     GetWeightsByNameReqInput,
+    InitWeightsSendGroupForRemoteInstanceReqInput,
     InitWeightsUpdateGroupReqInput,
     LoadLoRAAdapterReqInput,
     OpenSessionReqInput,
@@ -80,6 +81,7 @@ from sglang.srt.managers.io_struct import (
     ProfileReqInput,
     ReleaseMemoryOccupationReqInput,
     ResumeMemoryOccupationReqInput,
+    SendWeightsToRemoteInstanceReqInput,
     SeparateReasoningReqInput,
     SetInternalStateReq,
     SlowDownReqInput,
@@ -670,6 +672,38 @@ async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: R
         )
 
 
+@app.post("/init_weights_send_group_for_remote_instance")
+async def init_weights_send_group_for_remote_instance(
+    obj: InitWeightsSendGroupForRemoteInstanceReqInput, request: Request
+):
+    success, message = (
+        await _global_state.tokenizer_manager.init_weights_send_group_for_remote_instance(
+            obj, request
+        )
+    )
+    content = {"success": success, "message": message}
+    if success:
+        return ORJSONResponse(content, status_code=200)
+    else:
+        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
+
+
+@app.post("/send_weights_to_remote_instance")
+async def send_weights_to_remote_instance(
+    obj: SendWeightsToRemoteInstanceReqInput, request: Request
+):
+    success, message = (
+        await _global_state.tokenizer_manager.send_weights_to_remote_instance(
+            obj, request
+        )
+    )
+    content = {"success": success, "message": message}
+    if success:
+        return ORJSONResponse(content, status_code=200)
+    else:
+        return ORJSONResponse(content, status_code=HTTPStatus.BAD_REQUEST)
+
+
 @app.post("/init_weights_update_group")
 async def init_weights_update_group(
     obj: InitWeightsUpdateGroupReqInput, request: Request

python/sglang/srt/managers/io_struct.py

@@ -1020,6 +1020,44 @@ class UpdateWeightsFromTensorReqOutput:
     message: str
 
 
+@dataclass
+class InitWeightsSendGroupForRemoteInstanceReqInput:
+    # The master address
+    master_address: str
+    # The ports for each rank's communication group
+    ports: str
+    # The rank in the communication group
+    group_rank: int
+    # The world size
+    world_size: int
+    # The group name
+    group_name: str = "weight_send_group"
+    # The backend
+    backend: str = "nccl"
+
+
+@dataclass
+class InitWeightsSendGroupForRemoteInstanceReqOutput:
+    success: bool
+    message: str
+
+
+@dataclass
+class SendWeightsToRemoteInstanceReqInput:
+    # The master address
+    master_address: str
+    # The ports for each rank's communication group
+    ports: str
+    # The group name
+    group_name: str = "weight_send_group"
+
+
+@dataclass
+class SendWeightsToRemoteInstanceReqOutput:
+    success: bool
+    message: str
+
+
 @dataclass
 class InitWeightsUpdateGroupReqInput:
     # The master address

python/sglang/srt/managers/scheduler.py

@@ -81,6 +81,8 @@ from sglang.srt.managers.io_struct import (
     GetInternalStateReqOutput,
     GetWeightsByNameReqInput,
     HealthCheckOutput,
+    InitWeightsSendGroupForRemoteInstanceReqInput,
+    InitWeightsSendGroupForRemoteInstanceReqOutput,
     InitWeightsUpdateGroupReqInput,
     LoadLoRAAdapterReqInput,
     LoadLoRAAdapterReqOutput,
@@ -93,6 +95,8 @@ from sglang.srt.managers.io_struct import (
     ResumeMemoryOccupationReqInput,
     RpcReqInput,
     RpcReqOutput,
+    SendWeightsToRemoteInstanceReqInput,
+    SendWeightsToRemoteInstanceReqOutput,
     SetInternalStateReq,
     SetInternalStateReqOutput,
     SlowDownReqInput,
@@ -538,6 +542,14 @@ class Scheduler(
                 (CloseSessionReqInput, self.close_session),
                 (UpdateWeightFromDiskReqInput, self.update_weights_from_disk),
                 (InitWeightsUpdateGroupReqInput, self.init_weights_update_group),
+                (
+                    InitWeightsSendGroupForRemoteInstanceReqInput,
+                    self.init_weights_send_group_for_remote_instance,
+                ),
+                (
+                    SendWeightsToRemoteInstanceReqInput,
+                    self.send_weights_to_remote_instance,
+                ),
                 (
                     UpdateWeightsFromDistributedReqInput,
                     self.update_weights_from_distributed,
@@ -2429,6 +2441,22 @@ class Scheduler(
         self.send_to_detokenizer.send_pyobj(recv_req)
         return recv_req
 
+    def init_weights_send_group_for_remote_instance(
+        self, recv_req: InitWeightsSendGroupForRemoteInstanceReqInput
+    ):
+        """Init the seed and client instance communication group."""
+        success, message = self.tp_worker.init_weights_send_group_for_remote_instance(
+            recv_req
+        )
+        return InitWeightsSendGroupForRemoteInstanceReqOutput(success, message)
+
+    def send_weights_to_remote_instance(
+        self, recv_req: SendWeightsToRemoteInstanceReqInput
+    ):
+        """Send the seed instance weights to the destination instance."""
+        success, message = self.tp_worker.send_weights_to_remote_instance(recv_req)
+        return SendWeightsToRemoteInstanceReqOutput(success, message)
+
     def slow_down(self, recv_req: SlowDownReqInput):
         t = recv_req.forward_sleep_time
         if t is not None and t <= 0:

python/sglang/srt/managers/tokenizer_communicator_mixin.py

@@ -30,6 +30,8 @@ from sglang.srt.managers.io_struct import (
     GetInternalStateReqOutput,
     GetWeightsByNameReqInput,
     GetWeightsByNameReqOutput,
+    InitWeightsSendGroupForRemoteInstanceReqInput,
+    InitWeightsSendGroupForRemoteInstanceReqOutput,
     InitWeightsUpdateGroupReqInput,
     InitWeightsUpdateGroupReqOutput,
     LoadLoRAAdapterReqInput,
@@ -43,6 +45,8 @@ from sglang.srt.managers.io_struct import (
     ReleaseMemoryOccupationReqOutput,
     ResumeMemoryOccupationReqInput,
     ResumeMemoryOccupationReqOutput,
+    SendWeightsToRemoteInstanceReqInput,
+    SendWeightsToRemoteInstanceReqOutput,
     SetInternalStateReq,
     SetInternalStateReqOutput,
     SlowDownReqInput,
@@ -119,6 +123,12 @@ class TokenizerCommunicatorMixin:
         self.update_weights_from_distributed_communicator = _Communicator(
             self.send_to_scheduler, server_args.dp_size
         )
+        self.init_weights_send_group_for_remote_instance_communicator = _Communicator(
+            self.send_to_scheduler, server_args.dp_size
+        )
+        self.send_weights_to_remote_instance_communicator = _Communicator(
+            self.send_to_scheduler, server_args.dp_size
+        )
         self.update_weights_from_tensor_communicator = _Communicator(
             self.send_to_scheduler, server_args.dp_size
         )
@@ -169,6 +179,14 @@ class TokenizerCommunicatorMixin:
                     UpdateWeightsFromDistributedReqOutput,
                     self.update_weights_from_distributed_communicator.handle_recv,
                 ),
+                (
+                    InitWeightsSendGroupForRemoteInstanceReqOutput,
+                    self.init_weights_send_group_for_remote_instance_communicator.handle_recv,
+                ),
+                (
+                    SendWeightsToRemoteInstanceReqOutput,
+                    self.send_weights_to_remote_instance_communicator.handle_recv,
+                ),
                 (
                     UpdateWeightsFromTensorReqOutput,
                     self.update_weights_from_tensor_communicator.handle_recv,
@@ -310,6 +328,34 @@ class TokenizerCommunicatorMixin:
             result = (await self.update_weights_from_distributed_communicator(obj))[0]
             return result.success, result.message
 
+    async def init_weights_send_group_for_remote_instance(
+        self,
+        obj: InitWeightsSendGroupForRemoteInstanceReqInput,
+        request: Optional[fastapi.Request] = None,
+    ) -> Tuple[bool, str]:
+        self.auto_create_handle_loop()
+        # TODO: support DP
+        assert (
+            self.server_args.dp_size == 1
+        ), "dp_size must be 1 for init_weights_send_group_for_remote_instance"
+        result = (
+            await self.init_weights_send_group_for_remote_instance_communicator(obj)
+        )[0]
+        return result.success, result.message
+
+    async def send_weights_to_remote_instance(
+        self,
+        obj: SendWeightsToRemoteInstanceReqInput,
+        request: Optional[fastapi.Request] = None,
+    ) -> Tuple[bool, str]:
+        self.auto_create_handle_loop()
+        # TODO: support DP
+        assert (
+            self.server_args.dp_size == 1
+        ), "dp_size must be 1 for send_weights_to_remote_instance"
+        result = (await self.send_weights_to_remote_instance_communicator(obj))[0]
+        return result.success, result.message
+
     async def update_weights_from_tensor(
         self: TokenizerManager,
         obj: UpdateWeightsFromTensorReqInput,

python/sglang/srt/managers/tp_worker.py

@@ -30,8 +30,10 @@ from sglang.srt.hf_transformers_utils import (
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.io_struct import (
     GetWeightsByNameReqInput,
+    InitWeightsSendGroupForRemoteInstanceReqInput,
     InitWeightsUpdateGroupReqInput,
     LoadLoRAAdapterReqInput,
+    SendWeightsToRemoteInstanceReqInput,
     UnloadLoRAAdapterReqInput,
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
@@ -88,6 +90,7 @@ class TpModelWorker:
                 else server_args.speculative_draft_model_revision
             ),
             is_draft_model=is_draft_worker,
+            tp_rank=tp_rank,
         )
 
         self.model_runner = ModelRunner(
@@ -292,6 +295,31 @@ class TpModelWorker:
         )
         return success, message
 
+    def init_weights_send_group_for_remote_instance(
+        self, recv_req: InitWeightsSendGroupForRemoteInstanceReqInput
+    ):
+        success, message = (
+            self.model_runner.init_weights_send_group_for_remote_instance(
+                recv_req.master_address,
+                recv_req.ports,
+                recv_req.group_rank,
+                recv_req.world_size,
+                recv_req.group_name,
+                recv_req.backend,
+            )
+        )
+        return success, message
+
+    def send_weights_to_remote_instance(
+        self, recv_req: SendWeightsToRemoteInstanceReqInput
+    ):
+        success, message = self.model_runner.send_weights_to_remote_instance(
+            recv_req.master_address,
+            recv_req.ports,
+            recv_req.group_name,
+        )
+        return success, message
+
     def update_weights_from_distributed(
         self, recv_req: UpdateWeightsFromDistributedReqInput
     ):

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -26,8 +26,10 @@ import torch
 
 from sglang.srt.managers.io_struct import (
     GetWeightsByNameReqInput,
+    InitWeightsSendGroupForRemoteInstanceReqInput,
     InitWeightsUpdateGroupReqInput,
     LoadLoRAAdapterReqInput,
+    SendWeightsToRemoteInstanceReqInput,
     UnloadLoRAAdapterReqInput,
     UpdateWeightFromDiskReqInput,
     UpdateWeightsFromDistributedReqInput,
@@ -267,6 +269,20 @@ class TpModelWorkerClient:
         success, message = self.worker.init_weights_update_group(recv_req)
         return success, message
 
+    def init_weights_send_group_for_remote_instance(
+        self, recv_req: InitWeightsSendGroupForRemoteInstanceReqInput
+    ):
+        success, message = self.worker.init_weights_send_group_for_remote_instance(
+            recv_req
+        )
+        return success, message
+
+    def send_weights_to_remote_instance(
+        self, recv_req: SendWeightsToRemoteInstanceReqInput
+    ):
+        success, message = self.worker.send_weights_to_remote_instance(recv_req)
+        return success, message
+
     def update_weights_from_distributed(
         self, recv_req: UpdateWeightsFromDistributedReqInput
     ):

python/sglang/srt/model_executor/model_runner.py

@@ -19,18 +19,23 @@ import inspect
 import json
 import logging
 import os
+import socket
+import threading
 import time
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
+from urllib.parse import urlparse
 
+import requests
 import torch
 import torch.distributed as dist
 
 from sglang.srt.configs.device_config import DeviceConfig
-from sglang.srt.configs.load_config import LoadConfig
+from sglang.srt.configs.load_config import LoadConfig, LoadFormat
 from sglang.srt.configs.model_config import AttentionArch, ModelConfig
 from sglang.srt.configs.update_config import adjust_config_with_unaligned_cpu_tp
+from sglang.srt.connector import ConnectorType
 from sglang.srt.constants import GPU_MEMORY_TYPE_WEIGHTS
 from sglang.srt.distributed import (
     get_pp_group,
@@ -106,6 +111,9 @@ from sglang.srt.offloader import (
     set_offloader,
 )
 from sglang.srt.patch_torch import monkey_patch_torch_reductions
+from sglang.srt.remote_instance_weight_loader_utils import (
+    trigger_init_weights_send_group_for_remote_instance_request,
+)
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
@@ -128,6 +136,7 @@ from sglang.srt.utils import (
     is_sm100_supported,
     monkey_patch_p2p_access_check,
     monkey_patch_vllm_gguf_config,
+    parse_connector_type,
     set_cuda_arch,
 )
 from sglang.srt.weight_sync.tensor_bucket import (
@@ -256,6 +265,7 @@ class ModelRunner:
 
         # For weight updates
         self._model_update_group = {}
+        self._weights_send_group = {}
 
     def initialize(self, min_per_gpu_memory: float):
         server_args = self.server_args
@@ -726,6 +736,20 @@ class ModelRunner:
         if self.server_args.load_format == "gguf":
             monkey_patch_vllm_gguf_config()
 
+        if self.server_args.load_format == LoadFormat.REMOTE_INSTANCE:
+            if self.tp_rank == 0:
+                instance_ip = socket.gethostbyname(socket.gethostname())
+                t = threading.Thread(
+                    target=trigger_init_weights_send_group_for_remote_instance_request,
+                    args=(
+                        self.server_args.remote_instance_weight_loader_seed_instance_ip,
+                        self.server_args.remote_instance_weight_loader_seed_instance_service_port,
+                        self.server_args.remote_instance_weight_loader_send_weights_group_ports,
+                        instance_ip,
+                    ),
+                )
+                t.start()
+
         # Load the model
         # Remove monkey_patch when linear.py quant remove dependencies with vllm
         monkey_patch_vllm_parallel_state()
@@ -735,7 +759,7 @@ class ModelRunner:
             self.model = get_model(
                 model_config=self.model_config,
                 load_config=self.load_config,
-                device_config=DeviceConfig(self.device),
+                device_config=DeviceConfig(self.device, self.gpu_id),
             )
         monkey_patch_vllm_parallel_state(reverse=True)
         monkey_patch_isinstance_for_vllm_base_layer(reverse=True)
@@ -867,6 +891,103 @@ class ModelRunner:
         logger.info("Update weights end.")
         return True, "Succeeded to update model weights."
 
+    def init_weights_send_group_for_remote_instance(
+        self,
+        master_address,
+        ports,
+        group_rank,
+        world_size,
+        group_name,
+        backend="nccl",
+    ):
+        assert (
+            torch.distributed.is_initialized()
+        ), "Default torch process group must be initialized"
+        assert group_name != "", "Group name cannot be empty"
+
+        ports_list = ports.split(",")
+        assert (
+            len(ports_list) == self.tp_size
+        ), f"Expected {self.tp_size} ports, but got {len(ports_list)} ports."
+        group_port = ports_list[self.tp_rank]
+        group_name = f"{group_name}_{group_port}_{self.tp_rank}"
+
+        logger.info(
+            f"init custom process group: tp_rank={self.tp_rank}, gpu_id={self.gpu_id}, master_address={master_address}, master_port={group_port}, "
+            f"group_rank={group_rank}, world_size={world_size}, group_name={group_name}, backend={backend}"
+        )
+
+        torch.cuda.empty_cache()
+        success = False
+        message = ""
+        try:
+            self._weights_send_group[group_name] = init_custom_process_group(
+                backend=backend,
+                init_method=f"tcp://{master_address}:{group_port}",
+                world_size=world_size,
+                rank=group_rank,
+                group_name=group_name,
+                device_id=torch.device("cuda", self.gpu_id),
+            )
+            dist.barrier(group=self._weights_send_group[group_name])
+            success = True
+            message = (
+                f"Succeeded to init group through {master_address}:{group_port} group."
+            )
+        except Exception as e:
+            message = f"Failed to init group: {e}."
+            logger.error(message)
+
+        torch.cuda.empty_cache()
+        return success, message
+
+    def send_weights_to_remote_instance(
+        self,
+        master_address,
+        ports,
+        group_name,
+    ):
+        assert (
+            torch.distributed.is_initialized()
+        ), "Default torch process group must be initialized"
+        assert group_name != "", "Group name cannot be empty"
+
+        ports_list = ports.split(",")
+        assert (
+            len(ports_list) == self.tp_size
+        ), f"Expected {self.tp_size} ports, but got {len(ports_list)} ports."
+        group_port = ports_list[self.tp_rank]
+        group_name = f"{group_name}_{group_port}_{self.tp_rank}"
+
+        if self._weights_send_group[group_name] is not None:
+            send_group = self._weights_send_group[group_name]
+        else:
+            message = f"Group {group_name} not in _weights_send_group list. Please call `init_weights_send_group_for_remote_instance` first."
+            logger.error(message)
+            return False, message
+
+        torch.cuda.empty_cache()
+        success = False
+        message = ""
+        try:
+            for _, weights in self.model.named_parameters():
+                torch.distributed.broadcast(
+                    weights,
+                    src=0,
+                    group=send_group,
+                )
+            success = True
+            message = f"Succeeded to send weights through {master_address}:{group_port} {group_name}."
+        except Exception as e:
+            message = f"Failed to send weights: {e}."
+            logger.error(message)
+
+        # destroy the process group after sending weights
+        del self._weights_send_group[group_name]
+        torch.distributed.distributed_c10d.destroy_process_group(send_group)
+        torch.cuda.empty_cache()
+        return success, message
+
     def init_weights_update_group(
         self,
         master_address,

python/sglang/srt/model_loader/loader.py

@@ -12,6 +12,9 @@ import json
 import logging
 import math
 import os
+import re
+import socket
+import threading
 import time
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
@@ -27,9 +30,11 @@ from typing import (
     Tuple,
     cast,
 )
+from urllib.parse import urlparse
 
 import huggingface_hub
 import numpy as np
+import requests
 import safetensors.torch
 import torch
 from huggingface_hub import HfApi, hf_hub_download
@@ -56,6 +61,7 @@ from sglang.srt.model_loader.utils import (
 )
 from sglang.srt.model_loader.weight_utils import (
     _BAR_FORMAT,
+    default_weight_loader,
     download_safetensors_index_file_from_hf,
     download_weights_from_hf,
     filter_duplicate_safetensors_files,
@@ -71,6 +77,9 @@ from sglang.srt.model_loader.weight_utils import (
     safetensors_weights_iterator,
     set_runai_streamer_env,
 )
+from sglang.srt.remote_instance_weight_loader_utils import (
+    trigger_transferring_weights_request,
+)
 from sglang.srt.utils import (
     get_bool_env_var,
     get_device_capability,
@@ -1380,6 +1389,104 @@ class GGUFModelLoader(BaseModelLoader):
         return model
 
 
+class RemoteInstanceModelLoader(BaseModelLoader):
+    """Model loader that can load Tensors from remote sglang instance."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(
+                f"Model loader extra config is not supported for "
+                f"load format {load_config.load_format}"
+            )
+
+    def download_model(self, model_config: ModelConfig) -> None:
+        raise NotImplementedError
+
+    def load_model(
+        self,
+        *,
+        model_config: ModelConfig,
+        device_config: DeviceConfig,
+    ) -> nn.Module:
+        logger.info("Loading weights from remote instance ...")
+        load_config = self.load_config
+
+        assert load_config.load_format == LoadFormat.REMOTE_INSTANCE, (
+            f"Model loader {self.load_config.load_format} is not supported for "
+            f"load format {load_config.load_format}"
+        )
+
+        model_weights = f"instance://{model_config.remote_instance_weight_loader_seed_instance_ip}:{model_config.remote_instance_weight_loader_send_weights_group_ports[model_config.tp_rank]}"
+
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config)
+
+            with create_remote_connector(model_weights, device_config.device) as client:
+                connector_type = get_connector_type(client)
+                if connector_type == ConnectorType.INSTANCE:
+                    self.load_model_from_remote_instance(
+                        model, client, model_config, device_config
+                    )
+                else:
+                    raise ValueError(
+                        f"Unsupported connector type {connector_type} for "
+                        f"remote tensor model loading."
+                    )
+        return model.eval()
+
+    def load_model_from_remote_instance(
+        self, model, client, model_config: ModelConfig, device_config: DeviceConfig
+    ) -> nn.Module:
+        instance_ip = socket.gethostbyname(socket.gethostname())
+        start_build_group_tic = time.time()
+        client.build_group(
+            gpu_id=device_config.gpu_id,
+            tp_rank=model_config.tp_rank,
+            instance_ip=instance_ip,
+        )
+        torch.cuda.synchronize()
+        end_build_group_tic = time.time()
+        logger.debug(
+            f"finish building group for remote instance, time used: {(end_build_group_tic - start_build_group_tic):.4f}s"
+        )
+
+        if model_config.tp_rank == 0:
+            t = threading.Thread(
+                target=trigger_transferring_weights_request,
+                args=(
+                    model_config.remote_instance_weight_loader_seed_instance_ip,
+                    model_config.remote_instance_weight_loader_seed_instance_service_port,
+                    model_config.remote_instance_weight_loader_send_weights_group_ports,
+                    instance_ip,
+                ),
+            )
+            t.start()
+
+        start_get_weights_tic = time.time()
+        with set_default_torch_dtype(model_config.dtype):
+            for _, tensor in model.named_parameters():
+                torch.distributed.broadcast(
+                    tensor.data,
+                    src=0,
+                    group=client._model_update_group,
+                )
+            torch.cuda.synchronize()
+
+            if hasattr(model, "post_load_weights"):
+                model.post_load_weights()
+        end_get_weights_tic = time.time()
+        logger.debug(
+            f"finish getting all weights from remote instance, time used: {(end_get_weights_tic - start_get_weights_tic):.4f}s"
+        )
+        # destroy the process group after loading weights
+        torch.distributed.distributed_c10d.destroy_process_group(
+            client._model_update_group
+        )
+        torch.cuda.empty_cache()
+
+
 class RemoteModelLoader(BaseModelLoader):
     """Model loader that can load Tensors from remote database."""
 
@@ -1581,4 +1688,7 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     if load_config.load_format == LoadFormat.REMOTE:
         return RemoteModelLoader(load_config)
 
+    if load_config.load_format == LoadFormat.REMOTE_INSTANCE:
+        return RemoteInstanceModelLoader(load_config)
+
     return DefaultModelLoader(load_config)

python/sglang/srt/remote_instance_weight_loader_utils.py

@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from typing import List
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+
+def trigger_init_weights_send_group_for_remote_instance_request(
+    remote_instance_weight_loader_seed_instance_ip: str,
+    remote_instance_weight_loader_seed_instance_service_port: int,
+    remote_instance_weight_loader_send_weights_group_ports: List[int],
+    remote_instance_weight_loader_client_id: str,
+):
+    seed_instance_service_url = f"http://{remote_instance_weight_loader_seed_instance_ip}:{remote_instance_weight_loader_seed_instance_service_port}"
+    # Only support loading weights from instance with same parallelism strategy.
+    # Per TP rank pair between seed and dst instances will build a communication group for sending weights.
+    # i.e. seed TP 0 <-> dst TP 0, seed TP 1 <-> dst TP 1, etc.
+    # Each communication group will have a world size 2.
+    try:
+        requests.post(
+            f"{seed_instance_service_url}/init_weights_send_group_for_remote_instance",
+            json={
+                "master_address": remote_instance_weight_loader_seed_instance_ip,
+                "ports": (
+                    ",".join(
+                        str(p)
+                        for p in remote_instance_weight_loader_send_weights_group_ports
+                    )
+                ),
+                "group_rank": 0,
+                "world_size": 2,
+                "group_name": f"send_weights_{remote_instance_weight_loader_client_id}",
+                "backend": "nccl",
+            },
+        )
+    except Exception as e:
+        logger.error(
+            f"Failed to trigger init_weights_send_group_for_remote_instance_request to seed instance {seed_instance_service_url}: {e}."
+        )
+        raise
+
+
+def trigger_transferring_weights_request(
+    remote_instance_weight_loader_seed_instance_ip: str,
+    remote_instance_weight_loader_seed_instance_service_port: int,
+    remote_instance_weight_loader_send_weights_group_ports: List[int],
+    remote_instance_weight_loader_client_id: str,
+):
+    seed_instance_service_url = f"http://{remote_instance_weight_loader_seed_instance_ip}:{remote_instance_weight_loader_seed_instance_service_port}"
+    try:
+        requests.post(
+            f"{seed_instance_service_url}/send_weights_to_remote_instance",
+            json={
+                "master_address": remote_instance_weight_loader_seed_instance_ip,
+                "ports": (
+                    ",".join(
+                        str(p)
+                        for p in remote_instance_weight_loader_send_weights_group_ports
+                    )
+                ),
+                "group_name": f"send_weights_{remote_instance_weight_loader_client_id}",
+            },
+        )
+    except Exception as e:
+        logger.error(f"Failed to trigger send weights to remote instance request: {e}")
+        raise

python/sglang/srt/server_args.py

@@ -19,10 +19,12 @@ import json
 import logging
 import os
 import random
+import socket
 import sys
 import tempfile
 from typing import List, Literal, Optional, Union
 
+from sglang.srt.connector import ConnectorType
 from sglang.srt.function_call.function_call_parser import FunctionCallParser
 from sglang.srt.hf_transformers_utils import check_gguf_file, get_config
 from sglang.srt.lora.lora_registry import LoRARef
@@ -42,7 +44,9 @@ from sglang.srt.utils import (
     is_sm100_supported,
     is_triton_kernels_available,
     is_valid_ipv6_address,
+    json_list_type,
     nullable_str,
+    parse_connector_type,
 )
 from sglang.utils import is_in_ci
 
@@ -61,6 +65,7 @@ LOAD_FORMAT_CHOICES = [
     "bitsandbytes",
     "layered",
     "remote",
+    "remote_instance",
 ]
 
 QUANTIZATION_CHOICES = [
@@ -387,6 +392,11 @@ class ServerArgs:
     custom_weight_loader: Optional[List[str]] = None
     weight_loader_disable_mmap: bool = False
 
+    # Remote instance weight loading
+    remote_instance_weight_loader_seed_instance_ip: Optional[str] = None
+    remote_instance_weight_loader_seed_instance_service_port: Optional[int] = None
+    remote_instance_weight_loader_send_weights_group_ports: Optional[List[int]] = None
+
     # For PD-Multiplexing
     enable_pdmux: bool = False
     sm_group_num: int = 3
@@ -445,6 +455,7 @@ class ServerArgs:
         # Set missing default values
         if self.tokenizer_path is None:
             self.tokenizer_path = self.model_path
+
         if self.served_model_name is None:
             self.served_model_name = self.model_path
         if self.device is None:
@@ -538,7 +549,8 @@ class ServerArgs:
             self.sampling_backend = "pytorch"
 
         # Model-specific adjustments
-        self.model_specific_adjustments()
+        if parse_connector_type(self.model_path) != ConnectorType.INSTANCE:
+            self.model_specific_adjustments()
 
         # Set kernel backends
         if self.device == "cpu":
@@ -818,12 +830,19 @@ class ServerArgs:
         ) and check_gguf_file(self.model_path):
             self.quantization = self.load_format = "gguf"
 
-        # Model loading
         if is_remote_url(self.model_path):
             self.load_format = "remote"
         if self.custom_weight_loader is None:
             self.custom_weight_loader = []
 
+        if self.load_format == "remote_instance":
+            if (
+                self.remote_instance_weight_loader_seed_instance_ip is None
+                or self.remote_instance_weight_loader_seed_instance_service_port is None
+                or self.remote_instance_weight_loader_send_weights_group_ports is None
+            ):
+                self.load_format = "auto"
+
         # PD disaggregation
         if self.disaggregation_mode == "decode":
             assert (
@@ -881,6 +900,24 @@ class ServerArgs:
             help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
             required=True,
         )
+        parser.add_argument(
+            "--remote-instance-weight-loader-seed-instance-ip",
+            type=str,
+            default=ServerArgs.remote_instance_weight_loader_seed_instance_ip,
+            help="The ip of the seed instance for loading weights from remote instance.",
+        )
+        parser.add_argument(
+            "--remote-instance-weight-loader-seed-instance-service-port",
+            type=int,
+            default=ServerArgs.remote_instance_weight_loader_seed_instance_service_port,
+            help="The service port of the seed instance for loading weights from remote instance.",
+        )
+        parser.add_argument(
+            "--remote-instance-weight-loader-send-weights-group-ports",
+            type=json_list_type,
+            default=ServerArgs.remote_instance_weight_loader_send_weights_group_ports,
+            help="The communication group ports for loading weights from remote instance.",
+        )
         parser.add_argument(
             "--tokenizer-path",
             type=str,

python/sglang/srt/utils.py

@@ -15,6 +15,7 @@
 
 from __future__ import annotations
 
+import argparse
 import asyncio
 import builtins
 import ctypes
@@ -1431,6 +1432,7 @@ def init_custom_process_group(
     store=None,
     group_name=None,
     pg_options=None,
+    device_id=None,
 ):
     from torch.distributed.distributed_c10d import (
         Backend,
@@ -1484,6 +1486,7 @@ def init_custom_process_group(
         group_name=group_name,
         **{pg_options_param_name: pg_options},
         timeout=timeout,
+        device_id=device_id,
     )
 
     _world.pg_group_ranks[pg] = {i: i for i in range(world_size)}
@@ -3046,3 +3049,12 @@ def numa_bind_to_node(node: int):
 
     libnuma.numa_run_on_node(ctypes.c_int(node))
     libnuma.numa_set_localalloc()
+
+
+def json_list_type(value):
+    try:
+        return json.loads(value)
+    except json.JSONDecodeError:
+        raise argparse.ArgumentTypeError(
+            f"Invalid JSON list: {value}. Please provide a valid JSON list."
+        )

test/srt/run_suite.py

@@ -123,6 +123,7 @@ suites = {
         TestFile("rl/test_update_weights_from_distributed.py", 103),
         TestFile("test_data_parallelism.py", 73),
         TestFile("test_dp_attention.py", 277),
+        TestFile("test_load_weights_from_remote_instance.py", 72),
         TestFile("test_patch_torch.py", 19),
         TestFile("test_release_memory_occupation.py", 127),
         TestFile("hicache/test_hicache_storage_file_backend.py", 400),
@@ -251,6 +252,7 @@ suite_amd = {
         TestFile("lora/test_lora_tp.py", 116),
         TestFile("rl/test_update_weights_from_distributed.py", 103),
         TestFile("test_data_parallelism.py", 73),
+        TestFile("test_load_weights_from_remote_instance.py", 72),
         TestFile("test_patch_torch.py", 19),
     ],
     "per-commit-4-gpu-amd": [

test/srt/test_load_weights_from_remote_instance.py

@@ -0,0 +1,384 @@
+"""Test loading weights from remote instance.
+
+This test suite simulates loading weights from a remote instance.
+Rank 0 represents the seed instance, while ranks 1 represents the
+new instance that needs to loading weights from the seed instance.
+
+Seed instance must be started in `Server` mode, while the dst instance
+can be either `Engine` mode or `Server` mode.
+
+Seed instance does not support concurrently serving multiple dst instances.
+User has to guarantee that there is only one dst instance trying to load
+weights from the seed instance at any time.
+
+"""
+
+import gc
+import os
+import random
+import unittest
+
+import numpy as np
+import requests
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import sglang as sgl
+from sglang.test.test_utils import (
+    DEFAULT_PORT_FOR_SRT_TEST_RUNNER,
+    DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+)
+from sglang.utils import terminate_process
+
+mp.set_start_method("spawn", force=True)
+
+
+def verify_params_close(params1, params2, error_msg):
+    """Verify if two parameter arrays are close enough."""
+    try:
+        assert np.allclose(np.array(params1), np.array(params2)), error_msg
+    except Exception as e:
+        print(f"Parameters not close for {error_msg}")
+        print("Params1:", np.array(params1))
+        print("Params2:", np.array(params2))
+        raise e
+
+
+def init_process(
+    rank,
+    param_queue,
+    truncate_size,
+    tp_size,
+    model_name,
+    backends,
+    checking_parameters,
+    seed_instance_ip,
+    seed_instance_service_port,
+    seed_instance_group_base_port,
+    event_seed_ready,
+    event_dst_ready_list,
+):
+    torch.cuda.set_device(rank)
+
+    if rank == 0:
+        init_process_seed(
+            rank,
+            param_queue,
+            truncate_size,
+            model_name,
+            checking_parameters,
+            tp_size,
+            event_seed_ready,
+            event_dst_ready_list,
+        )
+    elif rank in [1, 2]:
+        init_process_dst(
+            rank,
+            param_queue,
+            truncate_size,
+            model_name,
+            seed_instance_ip,
+            seed_instance_service_port,
+            seed_instance_group_base_port,
+            checking_parameters,
+            backends[rank - 1],
+            tp_size,
+            event_seed_ready,
+            event_dst_ready_list,
+        )
+
+
+def init_process_seed(
+    rank,
+    param_queue,
+    truncate_size,
+    model_name,
+    checking_parameters,
+    tp_size,
+    event_seed_ready,
+    event_dst_ready_list,
+):
+    # These two environment variables are very important
+    # to avoid unexpected behaviors of CUDA and NCCL.
+    os.environ["NCCL_CUMEM_ENABLE"] = "0"
+    os.environ["NCCL_NVLS_ENABLE"] = "0"
+
+    # Load model and get parameters
+    torch.cuda.set_device(rank)
+    torch.cuda.synchronize()
+
+    url = DEFAULT_URL_FOR_TEST
+    process = popen_launch_server(
+        model_name,
+        url,
+        timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+        other_args=(
+            "--base-gpu-id",
+            str(rank),
+            "--tp-size",
+            str(tp_size),
+        ),
+    )
+    torch.cuda.synchronize()
+
+    seed_params = []
+    # Get the weights of seed instance for correctness check.
+    for parameter_name in checking_parameters:
+        seed_params.append(
+            requests.get(
+                f"{url}/get_weights_by_name",
+                json={
+                    "name": parameter_name,
+                    "truncate_size": truncate_size,
+                },
+            ).json()
+        )
+    param_queue.put((f"seed_params", seed_params))
+
+    event_seed_ready.set()
+    for i in range(len(event_dst_ready_list)):
+        event_dst_ready_list[i].wait()
+    terminate_process(process)
+
+
+def init_process_dst(
+    rank,
+    param_queue,
+    truncate_size,
+    model_name,
+    seed_instance_ip,
+    seed_instance_service_port,
+    seed_instance_group_base_port,
+    checking_parameters,
+    backend,
+    tp_size,
+    event_seed_ready,
+    event_dst_ready_list,
+):
+    torch.cuda.set_device(rank * tp_size)
+    torch.cuda.synchronize()
+    base_gpu_id = rank * tp_size
+
+    event_seed_ready.wait()
+    print(f"rank {rank}, seed ready")
+    for i in range(rank - 1):
+        print(f"rank {rank}, wait dst {i}")
+        event_dst_ready_list[i].wait()
+
+    ports = []
+    for i in range(tp_size):
+        ports.append(seed_instance_group_base_port + (rank - 1) * tp_size + i)
+
+    if backend == "Engine":
+        print(f"[sgl] rank {rank} init engine")
+        engine = sgl.Engine(
+            model_path=model_name,
+            base_gpu_id=base_gpu_id,
+            tp_size=tp_size,
+            cuda_graph_max_bs=2,
+            tokenizer_path=model_name,
+            remote_instance_weight_loader_seed_instance_ip=seed_instance_ip,
+            remote_instance_weight_loader_seed_instance_service_port=seed_instance_service_port,
+            remote_instance_weight_loader_send_weights_group_ports=ports,
+            load_format="remote_instance",
+        )
+    else:
+        host, _, port = DEFAULT_URL_FOR_TEST.rpartition(":")
+        url = ":".join([host, str(int(port) + 10000 + rank)])
+
+        print(f"[sgl] rank {rank} init server on url: {url}")
+        process = popen_launch_server(
+            model_name,
+            url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=(
+                "--base-gpu-id",
+                str(base_gpu_id),
+                "--tp-size",
+                str(tp_size),
+                "--cuda-graph-max-bs",
+                2,
+                "--tokenizer-path",
+                model_name,
+                "--remote-instance-weight-loader-seed-instance-ip",
+                seed_instance_ip,
+                "--remote-instance-weight-loader-seed-instance-service-port",
+                seed_instance_service_port,
+                "--remote-instance-weight-loader-send-weights-group-ports",
+                f"[{','.join(str(port) for port in ports)}]",
+                "--load-format",
+                "remote_instance",
+            ),
+        )
+    torch.cuda.synchronize()
+
+    event_dst_ready_list[rank - 1].set()
+
+    # Get weights of destination instance loaded from remote instance.
+    dst_params = []
+    for parameter_name in checking_parameters:
+        dst_params.append(
+            engine.get_weights_by_name(parameter_name, truncate_size)
+            if backend == "Engine"
+            else requests.get(
+                f"{url}/get_weights_by_name",
+                json={"name": parameter_name, "truncate_size": truncate_size},
+            ).json()
+        )
+
+    param_queue.put((f"sgl_dp_{rank}_dst_params", dst_params))
+
+    # Shutdown the engine or terminate the server process.
+    if backend == "Engine":
+        engine.shutdown()
+    else:
+        terminate_process(process)
+
+
+def test_load_weights_from_remote_instance(
+    tp_size,
+    dp_size,
+    model_name,
+    backends,
+    truncate_size,
+    checking_parameters,
+    seed_instance_ip,
+    seed_instance_service_port,
+    seed_instance_group_base_port,
+):
+    print(
+        f"Testing model: {model_name} tp_size: {tp_size}, dp_size: {dp_size} backend: {backends}"
+    )
+    param_queue = mp.Queue()
+    results = {}
+    event_seed_ready = mp.Event()
+    event_dst_ready_list = []
+    for i in range(dp_size):
+        event_dst_ready = mp.Event()
+        event_dst_ready_list.append(event_dst_ready)
+
+    context = mp.spawn(
+        init_process,
+        args=(
+            param_queue,
+            truncate_size,
+            tp_size,
+            model_name,
+            backends,
+            checking_parameters,
+            seed_instance_ip,
+            seed_instance_service_port,
+            seed_instance_group_base_port,
+            event_seed_ready,
+            event_dst_ready_list,
+        ),
+        nprocs=1 + dp_size,
+        join=False,
+    )
+
+    while len(results) < (1 + dp_size):
+        try:
+            key, value = param_queue.get(timeout=5)
+            results[key] = value
+        except Exception as e:
+            if all(not p.is_alive() for p in context.processes):
+                break
+
+    context.join()
+
+    if len(results) != (1 + dp_size):
+        raise RuntimeError(
+            f"Expected {(1 + dp_size)} parameters but got {len(results)}"
+        )
+
+    params = {
+        "seed": results.get("seed_params"),
+        "sgl_dp_1_dest": results.get("sgl_dp_1_dst_params"),
+    }
+
+    if dp_size == 2:
+        dp2_params = {
+            "sgl_dp_2_dest": results.get("sgl_dp_2_dst_params"),
+        }
+        assert all(v is not None for v in dp2_params.values())
+        params.update(dp2_params)
+
+    # Check the correctness of weights loaded from remote instance
+    # by verifying the weights of seed instance and destination instance.
+    for i in range(len(params["seed"])):
+        verify_params_close(
+            params["seed"][i],
+            params["sgl_dp_1_dest"][i],
+            f"sgl_dp_1_dst_params rank {i}",
+        )
+
+        if dp_size == 2:
+            verify_params_close(
+                params["seed"][i],
+                params["sgl_dp_2_dest"][i],
+                f"sgl_dp_2_dst_params rank {i}",
+            )
+
+    # Delete the context and close the parameter queue.
+    del context
+    param_queue.close()
+    param_queue.join_thread()
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+class TestLoadWeightsFromRemoteInstance(CustomTestCase):
+
+    def test_load_weights_from_remote_instance(self):
+
+        assert torch.cuda.device_count() >= 2, "At least 2 GPUs are required"
+        # test_suits : tp, dp, model_name, backend, dst_instance_id
+        if is_in_ci():
+            mode = random.choice(["Engine", "Server"])
+            test_suits = [
+                (1, 1, DEFAULT_SMALL_MODEL_NAME_FOR_TEST, [mode]),
+            ]
+        else:
+            test_suits = [
+                (1, 1, DEFAULT_SMALL_MODEL_NAME_FOR_TEST, ["Engine"]),
+                (1, 1, DEFAULT_SMALL_MODEL_NAME_FOR_TEST, ["Sever"]),
+                (2, 2, DEFAULT_SMALL_MODEL_NAME_FOR_TEST, ["Engine", "Server"]),
+            ]
+
+        truncate_size = 10
+        checking_parameters = [
+            "model.embed_tokens.weight",
+            "model.layers.0.input_layernorm.weight",
+            "model.layers.1.self_attn.q_proj.weight",
+            "model.layers.2.self_attn.k_proj.weight",
+            "model.layers.3.self_attn.v_proj.weight",
+            "model.layers.4.self_attn.o_proj.weight",
+            "model.layers.5.mlp.gate_proj.weight",
+            "model.layers.6.mlp.up_proj.weight",
+            "model.layers.7.mlp.down_proj.weight",
+            "model.layers.8.post_attention_layernorm.weight",
+            "model.norm.weight",
+        ]
+
+        for tp_size, dp_size, model_name, backends in test_suits:
+            test_load_weights_from_remote_instance(
+                tp_size,
+                dp_size,
+                model_name,
+                backends,
+                truncate_size,
+                checking_parameters,
+                "127.0.0.1",
+                DEFAULT_PORT_FOR_SRT_TEST_RUNNER + 1000,
+                60000,
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()