Your current environment
The output of python collect_env.py
Collecting environment information...
uv is set
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
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 : 3.12.13 (main, Apr 14 2026, 14:29:00) [Clang 22.1.3 ] (64-bit runtime)
Python platform : Linux-6.8.0-134-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H200 NVL
GPU 1: NVIDIA H200 NVL
GPU 2: NVIDIA H200 NVL
GPU 3: NVIDIA H200 NVL
GPU 4: NVIDIA H200 NVL
GPU 5: NVIDIA H200 NVL
GPU 6: NVIDIA H200 NVL
GPU 7: NVIDIA H200 NVL
Nvidia driver version : 580.159.03
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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): 16
On-line CPU(s) list: 0-15
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480C
CPU family: 6
Model: 143
Thread(s) per core: 1
Core(s) per socket: 1
Socket(s): 16
Stepping: 8
BogoMIPS: 4000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq dtes64 vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Virtualization: VT-x
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 512 KiB (16 instances)
L1i cache: 512 KiB (16 instances)
L2 cache: 64 MiB (16 instances)
L3 cache: 256 MiB (16 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-15
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Unknown: No mitigations
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Vulnerability Vmscape: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.13
[pip3] numpy==2.3.5
[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==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvvm==13.2.78
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.8.10.post20260709
[pip3] torch==2.11.0+cu130
[pip3] torch-c-dlpack-ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchcodec==0.15.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.14.0
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.25.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV6 NV6 NV6 PHB PHB PHB PHB 0-15 0 N/A
GPU1 NV6 X NV6 NV6 PHB PHB PHB PHB 0-15 0 N/A
GPU2 NV6 NV6 X NV6 PHB PHB PHB PHB 0-15 0 N/A
GPU3 NV6 NV6 NV6 X PHB PHB PHB PHB 0-15 0 N/A
GPU4 PHB PHB PHB PHB X NV6 NV6 NV6 0-15 0 N/A
GPU5 PHB PHB PHB PHB NV6 X NV6 NV6 0-15 0 N/A
GPU6 PHB PHB PHB PHB NV6 NV6 X NV6 0-15 0 N/A
GPU7 PHB PHB PHB PHB NV6 NV6 NV6 X 0-15 0 N/A
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
==============================
Environment Variables
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_llmsvc1
🐛 Describe the bug
Startup script:
vllm serve /models/MiniMax-M3 --served-model-name MiniMax-M3 --port 8000 --reasoning-parser minimax_m3 --tensor-parallel-size 8 --gpu-memory-utilization 0.95 --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minimax_m3 --block-size 128 --enforce-eager
Startup is fine but error occured when running curl
#!/bin/sh
curl http://localhost:8000/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model": "MiniMax-M3",
"max_tokens": 4096,
"temperature": 0.1,
"messages": [
{"role": "user", "content": "Hi! I am tim!"},
{"role": "assistant", "content": "Hi tim! How can I help you today?"},
{"role": "user", "content": "Who am I?"},
{"role": "assistant", "content": "\n\n\n"}
]
}'
Error:
[flashinfer_all_reduce.py:75] Failed to initialize FlashInfer All Reduce workspace: CUDA driver error: invalid device ordinal.
detail log:
vllm log.txt
Before submitting a new issue...
Your current environment
The output of
python collect_env.py🐛 Describe the bug
Startup script:
vllm serve /models/MiniMax-M3 --served-model-name MiniMax-M3 --port 8000 --reasoning-parser minimax_m3 --tensor-parallel-size 8 --gpu-memory-utilization 0.95 --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minimax_m3 --block-size 128 --enforce-eager
Startup is fine but error occured when running curl
#!/bin/sh
curl http://localhost:8000/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model": "MiniMax-M3",
"max_tokens": 4096,
"temperature": 0.1,
"messages": [
{"role": "user", "content": "Hi! I am tim!"},
{"role": "assistant", "content": "Hi tim! How can I help you today?"},
{"role": "user", "content": "Who am I?"},
{"role": "assistant", "content": "\n\n\n"}
]
}'
Error:
[flashinfer_all_reduce.py:75] Failed to initialize FlashInfer All Reduce workspace: CUDA driver error: invalid device ordinal.
detail log:
vllm log.txt
Before submitting a new issue...