vllm - 💡(How to fix) Fix [Bug]: “max_model_len” in “--speculative-config” is invalid [1 comments, 2 participants]

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vllm-project/vllm#41456Fetched 2026-05-02 05:28:02
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Fix Action

Fix / Workaround

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

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 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) CPU E5-2620 v4 @ 2.10GHz CPU family: 6 Model: 79 Thread(s) per core: 1 Core(s) per socket: 8 Socket(s): 2 Stepping: 1 CPU max MHz: 2300.0000 CPU min MHz: 1200.0000 BogoMIPS: 4199.99 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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf 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 cdp_l3 invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d Virtualization: VT-x L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 4 MiB (16 instances) L3 cache: 40 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0,2,4,6,8,10,12,14 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15 Vulnerability Gather data sampling: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled Vulnerability Mds: Mitigation; Clear CPU buffers; SMT disabled Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT disabled 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 and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; IBPB disabled; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT disabled

Code Example

python collect_env.py
Collecting environment information...
==============================
        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                : version 3.22.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0] (64-bit runtime)
Python platform              : Linux-5.15.0-153-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.4.131
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: Tesla T10
GPU 1: Tesla T10
GPU 2: Tesla T10
GPU 3: Tesla T10

Nvidia driver version        : 550.144.03
cuDNN version                : Could not collect
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, 48 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) CPU E5-2620 v4 @ 2.10GHz
CPU family:                              6
Model:                                   79
Thread(s) per core:                      1
Core(s) per socket:                      8
Socket(s):                               2
Stepping:                                1
CPU max MHz:                             2300.0000
CPU min MHz:                             1200.0000
BogoMIPS:                                4199.99
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf 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 cdp_l3 invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
Virtualization:                          VT-x
L1d cache:                               512 KiB (16 instances)
L1i cache:                               512 KiB (16 instances)
L2 cache:                                4 MiB (16 instances)
L3 cache:                                40 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0,2,4,6,8,10,12,14
NUMA node1 CPU(s):                       1,3,5,7,9,11,13,15
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             KVM: Mitigation: Split huge pages
Vulnerability L1tf:                      Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled
Vulnerability Mds:                       Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Meltdown:                  Mitigation; PTI
Vulnerability Mmio stale data:           Mitigation; Clear CPU buffers; SMT disabled
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 and seccomp
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB disabled; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsx async abort:           Mitigation; Clear CPU buffers; SMT disabled

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.5
[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==1.18.0
[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==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==26.4.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==5.5.4
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.6                    pypi_0    pypi
[conda] numpy                     2.2.5                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.4.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.4.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     26.4.0                   pypi_0    pypi
[conda] torch                     2.10.0                   pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0                   pypi_0    pypi
[conda] torchvision               0.25.0                   pypi_0    pypi
[conda] transformers              5.5.4                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     PHB     SYS     0,2,4,6,8,10    0               N/A
GPU1    PHB      X      PHB     SYS     0,2,4,6,8,10    0               N/A
GPU2    PHB     PHB      X      SYS     0,2,4,6,8,10    0               N/A
GPU3    SYS     SYS     SYS      X      1,3,5,7,9,11    1               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
==============================
LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64:/usr/local/cuda-12.4/extras/CUPTI/lib64:/usr/local/cuda-12.4/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu:
CUDA_HOME=/usr/local/cuda-12.4
CUDA_HOME=/usr/local/cuda-12.4
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_kevin

---

vllm serve /models/Qwen3.6-27B-FP8 \
  --port 23334 --host 0.0.0.0 \
  --api-key sk-123456 \
  --tensor-parallel-size 4 \
  --max-model-len 200000 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --limit-mm-per-prompt '{"video": 0}' \
  --served-model-name llm21 \
  --gpu-memory-utilization 0.82 \
  --dtype half \
  --kv-cache-dtype auto \
  --enable-chunked-prefill \
  --model-impl vllm \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3,"max_model_len":200000}' \
  --performance-mode interactivity \
  --enable-prefix-caching \
  --disable-custom-all-reduce \
  --attention-config.backend FLASHINFER \
  --max_num_seqs 5


mtp models load max_model_len is 256K not 200000


(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:549] Resolved architecture: Qwen3_5ForConditionalGeneration
(APIServer pid=451139) WARNING 05-01 19:38:30 [model.py:2016] Casting torch.bfloat16 to torch.float16.
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:1678] Using max model len 200000
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:549] Resolved architecture: Qwen3_5MTP
(APIServer pid=451139) WARNING 05-01 19:38:30 [model.py:2016] Casting torch.bfloat16 to torch.float16.
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:1678] Using max model len 262144
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
python collect_env.py
Collecting environment information...
==============================
        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                : version 3.22.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0] (64-bit runtime)
Python platform              : Linux-5.15.0-153-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.4.131
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: Tesla T10
GPU 1: Tesla T10
GPU 2: Tesla T10
GPU 3: Tesla T10

Nvidia driver version        : 550.144.03
cuDNN version                : Could not collect
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, 48 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) CPU E5-2620 v4 @ 2.10GHz
CPU family:                              6
Model:                                   79
Thread(s) per core:                      1
Core(s) per socket:                      8
Socket(s):                               2
Stepping:                                1
CPU max MHz:                             2300.0000
CPU min MHz:                             1200.0000
BogoMIPS:                                4199.99
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf 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 cdp_l3 invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
Virtualization:                          VT-x
L1d cache:                               512 KiB (16 instances)
L1i cache:                               512 KiB (16 instances)
L2 cache:                                4 MiB (16 instances)
L3 cache:                                40 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0,2,4,6,8,10,12,14
NUMA node1 CPU(s):                       1,3,5,7,9,11,13,15
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             KVM: Mitigation: Split huge pages
Vulnerability L1tf:                      Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled
Vulnerability Mds:                       Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Meltdown:                  Mitigation; PTI
Vulnerability Mmio stale data:           Mitigation; Clear CPU buffers; SMT disabled
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 and seccomp
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB disabled; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsx async abort:           Mitigation; Clear CPU buffers; SMT disabled

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.5
[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==1.18.0
[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==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==26.4.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==5.5.4
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.6                    pypi_0    pypi
[conda] numpy                     2.2.5                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.4.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.4.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     26.4.0                   pypi_0    pypi
[conda] torch                     2.10.0                   pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.10.0                   pypi_0    pypi
[conda] torchvision               0.25.0                   pypi_0    pypi
[conda] transformers              5.5.4                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     PHB     SYS     0,2,4,6,8,10    0               N/A
GPU1    PHB      X      PHB     SYS     0,2,4,6,8,10    0               N/A
GPU2    PHB     PHB      X      SYS     0,2,4,6,8,10    0               N/A
GPU3    SYS     SYS     SYS      X      1,3,5,7,9,11    1               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
==============================
LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64:/usr/local/cuda-12.4/extras/CUPTI/lib64:/usr/local/cuda-12.4/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu:
CUDA_HOME=/usr/local/cuda-12.4
CUDA_HOME=/usr/local/cuda-12.4
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_kevin
</details>

🐛 Describe the bug

vllm serve /models/Qwen3.6-27B-FP8 \
  --port 23334 --host 0.0.0.0 \
  --api-key sk-123456 \
  --tensor-parallel-size 4 \
  --max-model-len 200000 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --limit-mm-per-prompt '{"video": 0}' \
  --served-model-name llm21 \
  --gpu-memory-utilization 0.82 \
  --dtype half \
  --kv-cache-dtype auto \
  --enable-chunked-prefill \
  --model-impl vllm \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3,"max_model_len":200000}' \
  --performance-mode interactivity \
  --enable-prefix-caching \
  --disable-custom-all-reduce \
  --attention-config.backend FLASHINFER \
  --max_num_seqs 5


mtp models load max_model_len is 256K not 200000


(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:549] Resolved architecture: Qwen3_5ForConditionalGeneration
(APIServer pid=451139) WARNING 05-01 19:38:30 [model.py:2016] Casting torch.bfloat16 to torch.float16.
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:1678] Using max model len 200000
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:549] Resolved architecture: Qwen3_5MTP
(APIServer pid=451139) WARNING 05-01 19:38:30 [model.py:2016] Casting torch.bfloat16 to torch.float16.
(APIServer pid=451139) INFO 05-01 19:38:30 [model.py:1678] Using max model len 262144

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extent analysis

TL;DR

The issue is likely caused by a mismatch between the specified max-model-len and the actual maximum model length used by the MTP model, which is 262144.

Guidance

  • Verify that the max-model-len parameter is correctly set to 262144 (256K) in the vllm serve command.
  • Check the MTP model configuration to ensure it is set to use the correct maximum model length.
  • Review the logging output to confirm that the correct maximum model length is being used.
  • Consider updating the max-model-len parameter in the vllm serve command to match the actual maximum model length used by the MTP model.

Example

No code snippet is provided as the issue seems to be related to configuration and model settings.

Notes

The issue may be specific to the MTP model and its configuration, and further investigation may be required to resolve the issue.

Recommendation

Apply workaround: update the max-model-len parameter in the vllm serve command to 262144 to match the actual maximum model length used by the MTP model.

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