Collecting environment information...
uv is set
==============================
System Info
==============================
OS : Ubuntu 24.04.3 LTS (aarch64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : Could not collect
CMake version : version 3.22.0
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.11.0+cu130
Is debug build : False
CUDA used to build PyTorch : 13.0
ROCM used to build PyTorch : N/A
XPU used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-6.8.0-1055-nvidia-64k-aarch64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 13.0.88
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA GB200
GPU 1: NVIDIA GB200
GPU 2: NVIDIA GB200
GPU 3: NVIDIA GB200
Nvidia driver version : 580.82.07
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: aarch64
CPU op-mode(s): 64-bit
Byte Order: Little Endian
CPU(s): 144
On-line CPU(s) list: 0-143
Vendor ID: ARM
Model name: Neoverse-V2
Model: 0
Thread(s) per core: 1
Core(s) per cluster: 72
Socket(s): -
Cluster(s): 2
Stepping: r0p0
Frequency boost: disabled
CPU(s) scaling MHz: 100%
CPU max MHz: 3402.0000
CPU min MHz: 81.0000
BogoMIPS: 2000.00
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti
L1d cache: 9 MiB (144 instances)
L1i cache: 9 MiB (144 instances)
L2 cache: 144 MiB (144 instances)
L3 cache: 228 MiB (2 instances)
NUMA node(s): 34
NUMA node0 CPU(s): 0-71
NUMA node1 CPU(s): 72-143
NUMA node2 CPU(s):
NUMA node3 CPU(s):
NUMA node4 CPU(s):
NUMA node5 CPU(s):
NUMA node6 CPU(s):
NUMA node7 CPU(s):
NUMA node8 CPU(s):
NUMA node9 CPU(s):
NUMA node10 CPU(s):
NUMA node11 CPU(s):
NUMA node12 CPU(s):
NUMA node13 CPU(s):
NUMA node14 CPU(s):
NUMA node15 CPU(s):
NUMA node16 CPU(s):
NUMA node17 CPU(s):
NUMA node18 CPU(s):
NUMA node19 CPU(s):
NUMA node20 CPU(s):
NUMA node21 CPU(s):
NUMA node22 CPU(s):
NUMA node23 CPU(s):
NUMA node24 CPU(s):
NUMA node25 CPU(s):
NUMA node26 CPU(s):
NUMA node27 CPU(s):
NUMA node28 CPU(s):
NUMA node29 CPU(s):
NUMA node30 CPU(s):
NUMA node31 CPU(s):
NUMA node32 CPU(s):
NUMA node33 CPU(s):
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Not affected
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Mitigation; CSV2, but not BHB
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.13
[pip3] numpy==2.2.0
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cccl==13.3.3.4.1
[pip3] nvidia-cuda-crt==13.3.73
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvcc==13.2.78
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cuda-tileiras==13.2.78
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.26.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.5.2
[pip3] nvidia-cutlass-dsl-libs-base==4.5.2
[pip3] nvidia-cutlass-dsl-libs-cu13==4.5.2
[pip3] nvidia-ml-py==13.610.43
[pip3] nvidia-nccl-cu13==2.30.7
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvvm==13.2.78
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260531
[pip3] torch==2.11.0+cu130
[pip3] torch-c-dlpack-ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchcodec==0.14.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.13.0
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.1.dev1+gc67d81fcf (git sha: c67d81fcf)
vLLM Build Flags:
CUDA Archs: 10.0a; ROCm: Disabled; XPU: Disabled
GPU Topology:
�[4mGPU0 GPU1 GPU2 GPU3 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 CPU Affinity NUMA Affinity GPU NUMA ID�[0m
GPU0 X NV18 NV18 NV18 NODE NODE NODE SYS SYS SYS SYS NODE SYS NODE 0-71 0 N/A
GPU1 NV18 X NV18 NV18 NODE NODE NODE SYS SYS SYS SYS NODE SYS NODE 0-71 0 N/A
GPU2 NV18 NV18 X NV18 SYS SYS SYS NODE NODE NODE NODE SYS NODE SYS 72-143 1 N/A
GPU3 NV18 NV18 NV18 X SYS SYS SYS NODE NODE NODE NODE SYS NODE SYS 72-143 1 N/A
NIC0 NODE NODE SYS SYS X NODE NODE SYS SYS SYS SYS PIX SYS NODE
NIC1 NODE NODE SYS SYS NODE X NODE SYS SYS SYS SYS NODE SYS PIX
NIC2 NODE NODE SYS SYS NODE NODE X SYS SYS SYS SYS NODE SYS NODE
NIC3 SYS SYS NODE NODE SYS SYS SYS X NODE NODE PIX SYS NODE SYS
NIC4 SYS SYS NODE NODE SYS SYS SYS NODE X NODE NODE SYS PIX SYS
NIC5 SYS SYS NODE NODE SYS SYS SYS NODE NODE X NODE SYS NODE SYS
NIC6 SYS SYS NODE NODE SYS SYS SYS PIX NODE NODE X SYS NODE SYS
NIC7 NODE NODE SYS SYS PIX NODE NODE SYS SYS SYS SYS X SYS NODE
NIC8 SYS SYS NODE NODE SYS SYS SYS NODE PIX NODE NODE SYS X SYS
NIC9 NODE NODE SYS SYS NODE PIX NODE SYS SYS SYS SYS NODE SYS X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_1
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7
NIC8: mlx5_8
NIC9: mlx5_9
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=GPU-064a1306-112f-335e-e4d3-b6499daee9c0,GPU-c7cf451d-ec69-bf38-e52b-a7f114f810d5,GPU-905bb5f5-21aa-20ee-6319-df54f213322b,GPU-983a3a22-e362-92ca-4da4-f9f951cb494f
CUDA_MAJOR=13
VLLM_USE_RUST_FRONTEND=1
VLLM_NCCL_SO_PATH=/opt/vllm/lib/python3.12/site-packages/nvidia/nccl/lib/libnccl.so.2
NVIDIA_REQUIRE_CUDA=cuda>=13.0 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571 brand=unknown,driver>=575,driver<576 brand=grid,driver>=575,driver<576 brand=tesla,driver>=575,driver<576 brand=nvidia,driver>=575,driver<576 brand=quadro,driver>=575,driver<576 brand=quadrortx,driver>=575,driver<576 brand=nvidiartx,driver>=575,driver<576 brand=vapps,driver>=575,driver<576 brand=vpc,driver>=575,driver<576 brand=vcs,driver>=575,driver<576 brand=vws,driver>=575,driver<576 brand=cloudgaming,driver>=575,driver<576
VLLM_USE_V2_MODEL_RUNNER=1
CUDA_CACHE_PATH=/var/cache/vllm/cuda
VLLM_SKIP_P2P_CHECK=1
VLLM_USE_DEEP_GEMM=1
NCCL_MNNVL_ENABLE=1
TORCH_CUDA_ARCH_LIST=10.0a
NCCL_VERSION=2.28.3-1
NCCL_SOCKET_IFNAME=eth0
NCCL_NVLS_ENABLE=1
VLLM_CACHE_ROOT=/var/cache/vllm/vllm
NVIDIA_GDRCOPY=enabled
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NCCL_P2P_LEVEL=NVL
VLLM_NIXL_SIDE_CHANNEL_HOST=10.244.26.235
VLLM_WORKER_MULTIPROC_METHOD=fork
NVIDIA_PRODUCT_NAME=CUDA
NVIDIA_MOFED=enabled
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=13.0.2
TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC=15
TORCH_NCCL_DUMP_ON_TIMEOUT=0
MAX_JOBS=40
VLLM_USE_NCCL_SYMM_MEM=1
LD_LIBRARY_PATH=/opt/vllm/lib64/python3.12/site-packages/torch/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/lib:/usr/local/lib64:/opt/nvshmem-3.3.24/lib:/opt/amazon/efa/lib:/opt/amazon/efa/lib64:/opt/ucx/lib:/opt/ucx/lib64:/opt/vllm/lib64/python3.12/site-packages/torch/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/compat:/usr/local/lib:/usr/local/lib64:/opt/nvshmem-3.3.24/lib:/opt/nvshmem-3.3.24/lib64:/usr/lib/x86_64-linux-gnu:/usr/lib/aarch64-linux-gnu/:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
VLLM_LOGGING_LEVEL=INFO
CUDA_MINOR=0
NCCL_CUMEM_ENABLE=1
NVIDIA_GDS=enabled
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
Your current environment
The output of
python collect_env.py🐛 Describe the bug
Problem
When VLLM_USE_NCCL_SYMM_MEM=1 is enabled alongside FULL_DECODE_ONLY CUDA graph capture in ModelRunner V2, initialization fails with:
Reproduction
VLLM_USE_NCCL_SYMM_MEM=1
--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}'
--tensor-parallel-size 8 --nnodes 2 (multi-node TP, so TP group uses NCCL symm mem backend)
Model with vocab embedding all-reduce (e.g. DeepSeek-V4 with vocab parallel embedding)
Root Cause
vllm/v1/worker/gpu/cudagraph_utils.pyenterstorch.cuda.graph()without callingset_graph_pool_id()first:When the forward pass inside
torch.cuda.graph()callstensor_model_parallel_all_reduce → all_reduce_symmetric_with_copy → nccl_symm_mem_context.__enter__(), the context manager checkstorch.cuda.is_current_stream_capturing()(True), then asserts_graph_pool_id is not None— but it's None because set_graph_pool_id was never called.Before submitting a new issue...