Your current environment

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uv is set

    System Info

==============================
OS : Ubuntu 24.04.2 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : Could not collect
CMake version : version 3.31.6
Libc version : glibc-2.39

==============================
PyTorch Info

PyTorch version : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A

==============================
Python Environment

Python version : 3.12.3 (main, Feb 4 2025, 14:48:35) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-5.15.0-94-generic-x86_64-with-glibc2.39

==============================
CUDA / GPU Info

Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration : GPU 0: NVIDIA H200
Nvidia driver version : 550.127.08
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True

==============================
CPU Info

Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 240
On-line CPU(s) list: 0-239
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8580
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 60
Socket(s): 2
Stepping: 2
BogoMIPS: 4000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust sgx bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd sgx_lc fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.6 MiB (120 instances)
L1i cache: 3.8 MiB (120 instances)
L2 cache: 240 MiB (120 instances)
L3 cache: 600 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142,144,146,148,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143,145,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239
Vulnerability Gather data sampling: 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 Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

==============================
Versions of relevant libraries

[pip3] flashinfer-python==0.5.3
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.17.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.3.4
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0
[pip3] torchao==0.16.0.dev20251228+cu126
[pip3] torchaudio==2.9.0
[pip3] torchvision==0.24.0
[pip3] transformers==4.57.3
[pip3] triton==3.5.0
[conda] Could not collect

==============================
vLLM Info

ROCM Version : Could not collect
vLLM Version : 0.13.0
vLLM Build Flags:
CUDA Archs: 7.5 8.0 8.6 9.0 10.0 12.0+PTX; ROCm: Disabled
GPU Topology:
GPU0 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX PIX PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A
NIC0 PIX X PIX PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC1 PIX PIX X PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC2 PIX PIX PIX X NODE NODE NODE SYS SYS SYS SYS SYS SYS
NIC3 NODE NODE NODE NODE X NODE NODE SYS SYS SYS SYS SYS SYS
NIC4 NODE NODE NODE NODE NODE X NODE SYS SYS SYS SYS SYS SYS
NIC5 NODE NODE NODE NODE NODE NODE X SYS SYS SYS SYS SYS SYS
NIC6 SYS SYS SYS SYS SYS SYS SYS X PIX PIX NODE NODE NODE
NIC7 SYS SYS SYS SYS SYS SYS SYS PIX X PIX NODE NODE NODE
NIC8 SYS SYS SYS SYS SYS SYS SYS PIX PIX X NODE NODE NODE
NIC9 SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE X NODE NODE
NIC10 SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE X NODE
NIC11 SYS SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE NODE 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
NIC10: mlx5_10
NIC11: mlx5_11

==============================
Environment Variables

NVIDIA_VISIBLE_DEVICES=GPU-a726d068-b889-f603-5dd6-161157a9cb3b
CUBLAS_VERSION=12.9.1.4
NVIDIA_REQUIRE_CUDA=cuda>=9.0
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0+PTX
NCCL_VERSION=2.27.3
NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
TORCH_NCCL_USE_COMM_NONBLOCKING=0
CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0
NVIDIA_PRODUCT_NAME=PyTorch
CUDA_VERSION=12.9.1.010
PYTORCH_VERSION=2.8.0a0+5228986
PYTORCH_BUILD_NUMBER=0
CUBLASMP_VERSION=0.4.0.789
CUDNN_FRONTEND_VERSION=1.12.0
CUDNN_VERSION=9.10.2.21
PYTORCH_HOME=/opt/pytorch/pytorch
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/torch/lib:/usr/local/lib/python3.12/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NVIDIA_BUILD_ID=177567386
CUDA_DRIVER_VERSION=575.57.08
PYTORCH_BUILD_VERSION=2.8.0a0+5228986
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
CUDA_MODULE_LOADING=LAZY
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
NVIDIA_PYTORCH_VERSION=25.06
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

🐛 Describe the bug

I tried running the new v1 engine with torchao quantization, but it feels like the "plumbing" between the model loader and the quantization backend is disconnected. I ran into three separate crashes just trying to get a model to start.

1. The "NoneType" Crash (Configuration is missing)

When you use --quantization torchao, the engine tries to set up a file lock to protect the model directory. However, it crashes because it's being passed a None object instead of the actual loading configuration.

• Where: vllm/model_executor/model_loader/online_quantization.py
• The Error: AttributeError: 'NoneType' object has no attribute 'download_dir'
• Why: The code hardcodes None when calling the configuration factory, so the factory has no idea where the model is stored on disk.

2. The Embedding Crash (Tensor metadata is lost)

Even after fixing the config issue, the model fails to load weights into the embedding layer. vLLM uses .data.copy_(), which "strips" the special torchao metadata off the tensors.

• Where: vllm/model_executor/layers/vocab_parallel_embedding.py
• The Error: AttributeError: 'Tensor' object has no attribute 'tensor_data_names'
• Why: By using .data, we bypass the torchao dispatcher. When torchao tries to verify the metadata during the copy, it finds nothing and crashes.

3. Missing CLI Flag

There is currently no way to tell vLLM which torchao format to use (like int4wo) because the --torchao-config flag wasn't added to the command-line argument list in arg_utils.py.

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