[https://nvbugs/5448754][fix] Download HF model for all nodes.

jenkins/L0_Test.groovy

@@ -1892,15 +1892,9 @@ def launchTestJobs(pipeline, testFilter, dockerNode=null)
     ]
     fullSet += SBSASlurmTestConfigs.keySet()
 
-    multiNodesSBSAConfigs = [
-        // Each stage test 1 testcase with 8 GPUs and 2 nodes.
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-1": ["gb200-multi-node", "l0_gb200_multi_nodes", 1, 6, 8, 2],
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-2": ["gb200-multi-node", "l0_gb200_multi_nodes", 2, 6, 8, 2],
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-3": ["gb200-multi-node", "l0_gb200_multi_nodes", 3, 6, 8, 2],
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-4": ["gb200-multi-node", "l0_gb200_multi_nodes", 4, 6, 8, 2],
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-5": ["gb200-multi-node", "l0_gb200_multi_nodes", 5, 6, 8, 2],
-        "GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-6": ["gb200-multi-node", "l0_gb200_multi_nodes", 6, 6, 8, 2],
-    ]
+    multiNodesSBSAConfigs = (1..7).collectEntries { i ->
+        ["GB200-8_GPUs-2_Nodes-PyTorch-Post-Merge-${i}".toString(), ["gb200-multi-node", "l0_gb200_multi_nodes", i, 7, 8, 2]]
+    }
     fullSet += multiNodesSBSAConfigs.keySet()
 
     if (env.targetArch == AARCH64_TRIPLE) {

tensorrt_llm/llmapi/llm_utils.py

@@ -7,13 +7,13 @@
 import weakref
 from dataclasses import asdict, dataclass, field
 from pathlib import Path
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
 from tqdm import tqdm
 
-from .._utils import (global_mpi_rank, mpi_barrier, mpi_broadcast, mpi_rank,
-                      release_gc)
+from .._utils import (global_mpi_rank, local_mpi_rank, mpi_barrier,
+                      mpi_broadcast, mpi_rank, release_gc)
 from ..auto_parallel import AutoParallelConfig
 # yapf: disable
 from ..bindings.executor import (BatchingType, CapacitySchedulerPolicy,
@@ -607,6 +607,17 @@ def workspace(self) -> Path:
             self._workspace, tempfile.TemporaryDirectory) else Path(
                 self._workspace)
 
+    def _submit_to_all_workers(
+        self,
+        task: Callable[..., Any],
+        *args,
+        **kwargs,
+    ) -> List[Any]:
+        if self.llm_args.parallel_config.is_multi_gpu:
+            return self.mpi_session.submit_sync(task, *args, **kwargs)
+        else:
+            return [task(*args, **kwargs)]
+
     def __call__(self) -> Tuple[Path, Union[Path, None]]:
 
         if self.llm_args.model_format is _ModelFormatKind.TLLM_ENGINE:
@@ -627,9 +638,11 @@ def __call__(self) -> Tuple[Path, Union[Path, None]]:
                     f'backend {self.llm_args.backend} is not supported.')
 
             if self.model_loader.model_obj.is_hub_model:
-                self._hf_model_dir = download_hf_model(
-                    self.model_loader.model_obj.model_name,
-                    self.llm_args.revision)
+                hf_model_dirs = self._submit_to_all_workers(
+                    CachedModelLoader._node_download_hf_model,
+                    model=self.model_loader.model_obj.model_name,
+                    revision=self.llm_args.revision)
+                self._hf_model_dir = hf_model_dirs[0]
             else:
                 self._hf_model_dir = self.model_loader.model_obj.model_dir
 
@@ -806,6 +819,17 @@ def build_task(engine_dir: Path):
 
         return self.get_engine_dir()
 
+    @print_traceback_on_error
+    @staticmethod
+    def _node_download_hf_model(
+        model: str,
+        revision: Optional[str] = None,
+    ) -> Optional[Path]:
+        if local_mpi_rank() == 0:
+            return download_hf_model(model, revision)
+        else:
+            return None
+
     @print_traceback_on_error
     @staticmethod
     def _node_build_task(

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -2028,16 +2028,25 @@ def test_fp8_block_scales(self, tp_size, pp_size, ep_size, attention_dp,
             task.evaluate(llm)
 
     @pytest.mark.parametrize(
-        "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler",
-        [(1, 1, 1, False, True, True)],
-        ids=["latency"])
+        "tp_size,pp_size,ep_size,attention_dp,cuda_graph,overlap_scheduler,is_cached",
+        [(1, 1, 1, False, True, True, True),
+         pytest.param(8,
+                      1,
+                      1,
+                      False,
+                      True,
+                      True,
+                      False,
+                      marks=pytest.mark.skip_less_mpi_world_size(8))],
+        ids=["latency", "multi_gpus_no_cache"])
     def test_bf16(self, tp_size, pp_size, ep_size, attention_dp, cuda_graph,
-                  overlap_scheduler):
+                  overlap_scheduler, is_cached):
         pytorch_config = dict(
             disable_overlap_scheduler=not overlap_scheduler,
             cuda_graph_config=CudaGraphConfig() if cuda_graph else None)
 
-        with LLM(f"{llm_models_root()}/Qwen3/Qwen3-8B",
+        with LLM(f"{llm_models_root()}/Qwen3/Qwen3-8B"
+                 if is_cached else "Qwen/Qwen3-8B",
                  tensor_parallel_size=tp_size,
                  pipeline_parallel_size=pp_size,
                  moe_expert_parallel_size=ep_size,

tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml

@@ -19,3 +19,4 @@ l0_gb200_multi_nodes:
   - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_cutlass] TIMEOUT (180)
   - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_trtllm] TIMEOUT (180)
   - accuracy/test_llm_api_pytorch.py::TestQwen3_235B_A22B::test_nvfp4[latency_moe_trtllm_eagle3] TIMEOUT (180)
+  - accuracy/test_llm_api_pytorch.py::TestQwen3_8B::test_bf16[multi_gpus_no_cache] TIMEOUT (180)