vllm - 💡(How to fix) Fix [Bug]: Eagle3 Speculative Decoding + tensor_parallel_size=2 + draft_tensor_parallel_size=2 causes NCCL timeout / collective deadlock

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…

Fix Action

Fix / Workaround

============================== 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): 64 On-line CPU(s) list: 0-63 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 1 Stepping: 6 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 4600.00 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 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 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust 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 wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 1.5 MiB (32 instances) L1i cache: 1 MiB (32 instances) L2 cache: 40 MiB (32 instances) L3 cache: 54 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-63 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable 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; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.4 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              : 2.11.0+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 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-6.8.0-1008-nvidia-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 RTX A5000
GPU 1: NVIDIA RTX A5000

Nvidia driver version        : 580.159.03
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, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               64
On-line CPU(s) list:                  0-63
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz
CPU family:                           6
Model:                                106
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            1
Stepping:                             6
CPU max MHz:                          3500.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4600.00
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 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 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust 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 wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            1.5 MiB (32 instances)
L1i cache:                            1 MiB (32 instances)
L2 cache:                             40 MiB (32 instances)
L3 cache:                             54 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-63
Vulnerability Gather data sampling:   Mitigation; Microcode
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
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; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.11.post2
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cccl==13.3.3.3.1
[pip3] nvidia-cuda-crt==13.3.33
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvcc==13.3.33
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[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==13.595.45
[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==13.3.33
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260530
[pip3] torch==2.11.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.9.0
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.11.post2             pypi_0    pypi
[conda] numpy                     2.3.5                    pypi_0    pypi
[conda] nvidia-cublas             13.1.0.3                 pypi_0    pypi
[conda] nvidia-cuda-cccl          13.3.3.3.1               pypi_0    pypi
[conda] nvidia-cuda-crt           13.3.33                  pypi_0    pypi
[conda] nvidia-cuda-cupti         13.0.85                  pypi_0    pypi
[conda] nvidia-cuda-nvcc          13.3.33                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc         13.0.88                  pypi_0    pypi
[conda] nvidia-cuda-runtime       13.0.96                  pypi_0    pypi
[conda] nvidia-cudnn-cu13         9.19.0.56                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft              12.0.0.61                pypi_0    pypi
[conda] nvidia-cufile             1.15.1.6                 pypi_0    pypi
[conda] nvidia-curand             10.4.0.35                pypi_0    pypi
[conda] nvidia-cusolver           12.0.4.66                pypi_0    pypi
[conda] nvidia-cusparse           12.6.3.3                 pypi_0    pypi
[conda] nvidia-cusparselt-cu13    0.8.0                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.5.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.5.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-cu13 4.5.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu13          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink          13.0.88                  pypi_0    pypi
[conda] nvidia-nvshmem-cu13       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx               13.0.85                  pypi_0    pypi
[conda] nvidia-nvvm               13.3.33                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] tokenspeed-triton         3.7.10.post20260530          pypi_0    pypi
[conda] torch                     2.11.0+cu130             pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.11.0+cu130             pypi_0    pypi
[conda] torchvision               0.26.0+cu130             pypi_0    pypi
[conda] transformers              5.9.0                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.22.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    0-63    0               N/A
GPU1    NODE     X      0-63    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
==============================
CUDA_VISIBLE_DEVICES=0,1
CUDA_VISIBLE_DEVICES=0,1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_liuzhu

---

from vllm import LLM, SamplingParams

prompts = ["The future of AI is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(
    model="/home/liuzhu/tank/在读/liuzhu/Qwen3-8B",
    tensor_parallel_size=2,
    speculative_config={
        "model": "/home/liuzhu/tank/在读/liuzhu/qwen3_8b_eagle3",
        "draft_tensor_parallel_size": 2, 
        "num_speculative_tokens": 2,
        "method": "eagle3",
    },
)

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.4 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              : 2.11.0+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 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-6.8.0-1008-nvidia-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 RTX A5000
GPU 1: NVIDIA RTX A5000

Nvidia driver version        : 580.159.03
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, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               64
On-line CPU(s) list:                  0-63
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz
CPU family:                           6
Model:                                106
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            1
Stepping:                             6
CPU max MHz:                          3500.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4600.00
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 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 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust 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 wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            1.5 MiB (32 instances)
L1i cache:                            1 MiB (32 instances)
L2 cache:                             40 MiB (32 instances)
L3 cache:                             54 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-63
Vulnerability Gather data sampling:   Mitigation; Microcode
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
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; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.11.post2
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cccl==13.3.3.3.1
[pip3] nvidia-cuda-crt==13.3.33
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvcc==13.3.33
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[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==13.595.45
[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==13.3.33
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260530
[pip3] torch==2.11.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu130
[pip3] torchvision==0.26.0+cu130
[pip3] transformers==5.9.0
[pip3] triton==3.6.0
[conda] flashinfer-python         0.6.11.post2             pypi_0    pypi
[conda] numpy                     2.3.5                    pypi_0    pypi
[conda] nvidia-cublas             13.1.0.3                 pypi_0    pypi
[conda] nvidia-cuda-cccl          13.3.3.3.1               pypi_0    pypi
[conda] nvidia-cuda-crt           13.3.33                  pypi_0    pypi
[conda] nvidia-cuda-cupti         13.0.85                  pypi_0    pypi
[conda] nvidia-cuda-nvcc          13.3.33                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc         13.0.88                  pypi_0    pypi
[conda] nvidia-cuda-runtime       13.0.96                  pypi_0    pypi
[conda] nvidia-cudnn-cu13         9.19.0.56                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.18.0                   pypi_0    pypi
[conda] nvidia-cufft              12.0.0.61                pypi_0    pypi
[conda] nvidia-cufile             1.15.1.6                 pypi_0    pypi
[conda] nvidia-curand             10.4.0.35                pypi_0    pypi
[conda] nvidia-cusolver           12.0.4.66                pypi_0    pypi
[conda] nvidia-cusparse           12.6.3.3                 pypi_0    pypi
[conda] nvidia-cusparselt-cu13    0.8.0                    pypi_0    pypi
[conda] nvidia-cutlass-dsl        4.5.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-base 4.5.2                    pypi_0    pypi
[conda] nvidia-cutlass-dsl-libs-cu13 4.5.2                    pypi_0    pypi
[conda] nvidia-ml-py              13.595.45                pypi_0    pypi
[conda] nvidia-nccl-cu13          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink          13.0.88                  pypi_0    pypi
[conda] nvidia-nvshmem-cu13       3.4.5                    pypi_0    pypi
[conda] nvidia-nvtx               13.0.85                  pypi_0    pypi
[conda] nvidia-nvvm               13.3.33                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] tokenspeed-triton         3.7.10.post20260530          pypi_0    pypi
[conda] torch                     2.11.0+cu130             pypi_0    pypi
[conda] torch-c-dlpack-ext        0.1.5                    pypi_0    pypi
[conda] torchaudio                2.11.0+cu130             pypi_0    pypi
[conda] torchvision               0.26.0+cu130             pypi_0    pypi
[conda] transformers              5.9.0                    pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.22.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    0-63    0               N/A
GPU1    NODE     X      0-63    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
==============================
CUDA_VISIBLE_DEVICES=0,1
CUDA_VISIBLE_DEVICES=0,1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_liuzhu
</details>

🐛 Describe the bug

When running the official EAGLE3 example from the vLLM docs with 2-GPU tensor parallelism, the engine immediately hits NCCL collective operation timeouts and deadlocks.

🔴 Reproduction code (official example)

from vllm import LLM, SamplingParams

prompts = ["The future of AI is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(
    model="/home/liuzhu/tank/在读/liuzhu/Qwen3-8B",
    tensor_parallel_size=2,
    speculative_config={
        "model": "/home/liuzhu/tank/在读/liuzhu/qwen3_8b_eagle3",
        "draft_tensor_parallel_size": 2, 
        "num_speculative_tokens": 2,
        "method": "eagle3",
    },
)

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Bug info: (EngineCore pid=729594) INFO 05-30 18:09:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). (EngineCore pid=729594) INFO 05-30 18:10:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). (EngineCore pid=729594) INFO 05-30 18:11:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). (EngineCore pid=729594) INFO 05-30 18:12:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). (EngineCore pid=729594) INFO 05-30 18:13:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). (EngineCore pid=729594) INFO 05-30 18:14:16 [shm_broadcast.py:698] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization).

Before submitting a new issue...

  • Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING

vllm - 💡(How to fix) Fix [Bug]: Eagle3 Speculative Decoding + tensor_parallel_size=2 + draft_tensor_parallel_size=2 causes NCCL timeout / collective deadlock