vllm - 💡(How to fix) Fix [Bug]: NIXL disagg fails for Qwen3.5 hybrid model when prefill TP4 and decode DP8 use different physical block sizes

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Qwen3.5 NVFP4 disaggregated serving with NIXL fails when using heterogeneous prefill/decode parallelism:

  • Prefill: 4xTEP4 (4 prefill workers, each TP4 + expert parallel, one 4xGB200 node per worker)
  • Decode: 1xDEP8 (1 logical decode worker, DP8 + expert parallel, spanning two 4xGB200 nodes)
  • KV cache dtype: bfloat16
  • Prefix caching disabled
  • Async scheduling disabled
  • Hybrid KV cache manager explicitly enabled
  • SSM conv state layout set to DS

The deployment becomes healthy, but decode NIXL handshakes fail at request time because the local decode block size is 16 and the remote prefill block size is 32. The connector currently asserts local_block_size % remote_block_size == 0, which rejects this case even though the opposite direction is divisible.

This makes the configuration unusable for correctness testing: GSM8K accuracy collapses because KV transfers fail and vLLM skips KV post-processing for affected requests.

Error Message

NIXL transfer failure: handshake_setup_failed AssertionError: Local block size 16 is not divisible by remote block size 32 or vice versa.

Root Cause

The deployment becomes healthy, but decode NIXL handshakes fail at request time because the local decode block size is 16 and the remote prefill block size is 32. The connector currently asserts local_block_size % remote_block_size == 0, which rejects this case even though the opposite direction is divisible.

Fix Action

Fix / Workaround

============================== 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 socket: 72 Socket(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 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 Ghostwrite: 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 Old microcode: 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

Code Example

==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (aarch64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : Could not collect
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-6.17.0-1014-nvidia-64k-aarch64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.61
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        : Could not collect
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 socket:                      72
Socket(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 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 Ghostwrite:                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 Old microcode:             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==0.6.8.post1
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[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==4.5.0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0
[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] pyzmq==27.1.0
[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==5.8.0
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.20.2rc1.dev265+gdd6b3a5ef.d20260512 (git sha: dd6b3a5ef, date: 20260512)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-71    0              N/A
GPU1    NV18     X      NV18    NV18    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-71    0              N/A
GPU2    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    72-143  1              N/A
GPU3    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    72-143  1              N/A
NIC0    NODE    NODE    SYS     SYS      X      NODE    NODE    NODE    SYS     SYS     SYS     SYS
NIC1    NODE    NODE    SYS     SYS     NODE     X      NODE    NODE    SYS     SYS     SYS     SYS
NIC2    NODE    NODE    SYS     SYS     NODE    NODE     X      PIX     SYS     SYS     SYS     SYS
NIC3    NODE    NODE    SYS     SYS     NODE    NODE    PIX      X      SYS     SYS     SYS     SYS
NIC4    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE
NIC5    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      NODE    NODE
NIC6    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE     X      PIX
NIC7    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE    PIX      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

==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/home/aperevedents/storage/cuda/cuda128/lib64:
CUDA_HOME=/home/aperevedents/storage/cuda/cuda128
CUDA_HOME=/home/aperevedents/storage/cuda/cuda128
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_aperevedents

---

prefill: 4 workers, each TP4 + expert-parallel on one 4-GPU GB200 node
decode:  1 logical worker, DP8 + expert-parallel across two 4-GPU GB200 nodes

VLLM_SSM_CONV_STATE_LAYOUT=DS
kv-cache-dtype=bfloat16
--no-enable-prefix-caching
--no-async-scheduling
--no-disable-hybrid-kv-cache-manager
--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail"}'

---

srtctl apply -f recipes/qwen3.5/nvfp4/disagg/stp_prefix_off/qwen35-nvfp4-4xTEP4-1xDEP8.yaml

---

NIXL transfer failure: handshake_setup_failed
AssertionError: Local block size 16 is not divisible by remote block size 32 or vice versa.

---

NIXL compatibility check passed
Transfer plan: TransferTopology(tp_ratio=-4, num_kv_heads=2, local_tp=1, remote_tp=4, local_rank=0, remote_block_len=32768)

---

NIXL transfer failure: handshake_failed
Skipping KV post-processing for failed request ...

---

Accuracy: 0.031
Invalid responses: 0.000
Total output tokens: 8192

---

(Worker_DP3_EP3 pid=349953) INFO 05-17 20:14:00 [worker.py:555] NIXL compatibility check passed (hash: 63cfb9d5c28916a5ccc703d83c44c6dec2009cdbaf85edbe0f9cec80e5762d81)
(Worker_DP3_EP3 pid=349953) INFO 05-17 20:14:00 [worker.py:1301] Transfer plan: TransferTopology(tp_ratio=-4, num_kv_heads=2, local_tp=1, remote_tp=4, local_rank=0, remote_block_len=32768)
(Worker_DP3_EP3 pid=349953) ERROR 05-17 20:14:00 [worker.py:735] NIXL transfer failure: handshake_setup_failed | Context: {'failure_type': 'handshake_setup_failed', 'request_id': None, 'engine_id': '6ff14cc5-a32c-46c9-80ed-801031f38e11_dp0', 'remote_engine_id': '944cb3df-ccb0-4860-9fae-15ec5c53a9ae'}
Traceback (most recent call last):
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 733, in done_callback
    self._remote_agents[eid] = f.result()
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/usr/lib/python3.12/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 581, in _nixl_handshake
    remote_agent_name = self.add_remote_agent(
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 1320, in add_remote_agent
    block_size_ratio = transfer_topo.block_size_ratio(nixl_agent_meta.block_size)
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/utils.py", line 519, in block_size_ratio
    assert self.block_size % remote_block_size == 0, (
AssertionError: Local block size 16 is not divisible by remote block size 32 or vice versa.
(Worker_DP3_EP3 pid=349953) WARNING 05-17 20:14:00 [worker.py:1735] Skipping KV post-processing for failed request 5a4c1f4d-e725-40c4-944f-6c0d0584f627-b6ed42d1
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (aarch64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : Could not collect
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-6.17.0-1014-nvidia-64k-aarch64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.61
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        : Could not collect
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 socket:                      72
Socket(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 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 Ghostwrite:                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 Old microcode:             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==0.6.8.post1
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[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==4.5.0
[pip3] nvidia-cutlass-dsl-libs-base==4.5.0
[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] pyzmq==27.1.0
[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==5.8.0
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.20.2rc1.dev265+gdd6b3a5ef.d20260512 (git sha: dd6b3a5ef, date: 20260512)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-71    0              N/A
GPU1    NV18     X      NV18    NV18    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-71    0              N/A
GPU2    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    72-143  1              N/A
GPU3    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    72-143  1              N/A
NIC0    NODE    NODE    SYS     SYS      X      NODE    NODE    NODE    SYS     SYS     SYS     SYS
NIC1    NODE    NODE    SYS     SYS     NODE     X      NODE    NODE    SYS     SYS     SYS     SYS
NIC2    NODE    NODE    SYS     SYS     NODE    NODE     X      PIX     SYS     SYS     SYS     SYS
NIC3    NODE    NODE    SYS     SYS     NODE    NODE    PIX      X      SYS     SYS     SYS     SYS
NIC4    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE
NIC5    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      NODE    NODE
NIC6    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE     X      PIX
NIC7    SYS     SYS     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE    PIX      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

==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/home/aperevedents/storage/cuda/cuda128/lib64:
CUDA_HOME=/home/aperevedents/storage/cuda/cuda128
CUDA_HOME=/home/aperevedents/storage/cuda/cuda128
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_aperevedents
</details>

🐛 Describe the bug

Summary

Qwen3.5 NVFP4 disaggregated serving with NIXL fails when using heterogeneous prefill/decode parallelism:

  • Prefill: 4xTEP4 (4 prefill workers, each TP4 + expert parallel, one 4xGB200 node per worker)
  • Decode: 1xDEP8 (1 logical decode worker, DP8 + expert parallel, spanning two 4xGB200 nodes)
  • KV cache dtype: bfloat16
  • Prefix caching disabled
  • Async scheduling disabled
  • Hybrid KV cache manager explicitly enabled
  • SSM conv state layout set to DS

The deployment becomes healthy, but decode NIXL handshakes fail at request time because the local decode block size is 16 and the remote prefill block size is 32. The connector currently asserts local_block_size % remote_block_size == 0, which rejects this case even though the opposite direction is divisible.

This makes the configuration unusable for correctness testing: GSM8K accuracy collapses because KV transfers fail and vLLM skips KV post-processing for affected requests.

Environment

  • Hardware: 4xGB200 per node
  • Nodes used in failing run: 6 nodes
  • Model: Qwen3.5-397B-A17B-NVFP4
  • vLLM version: 0.20.2rc1.dev354+g24337fb86.precompiled
  • Dynamo: 1.2.0.dev20260429
  • NIXL: nixl-cu13==1.0.1, nixl==1.0.1
  • CUDA base: CUDA 13 runtime

Related upstream work

I found related prior work around heterogeneous NIXL block sizes:

The RFC says support for nP < nD was completed, but support for nP > nD was dropped from scope. This bug appears to hit that unsupported direction in a Qwen3.5 hybrid/HMA configuration: prefill uses physical block size 32, decode uses physical block size 16, and decode-side NIXL rejects the transfer during handshake.

Failing configuration

The full resolved recipe is included in the attached log archive as 170987/logs/config.yaml. The important bits are:

prefill: 4 workers, each TP4 + expert-parallel on one 4-GPU GB200 node
decode:  1 logical worker, DP8 + expert-parallel across two 4-GPU GB200 nodes

VLLM_SSM_CONV_STATE_LAYOUT=DS
kv-cache-dtype=bfloat16
--no-enable-prefix-caching
--no-async-scheduling
--no-disable-hybrid-kv-cache-manager
--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail"}'

Launch:

srtctl apply -f recipes/qwen3.5/nvfp4/disagg/stp_prefix_off/qwen35-nvfp4-4xTEP4-1xDEP8.yaml

Expected behavior

NIXL should either:

  • support the reciprocal block-size case where decode local block size is smaller than prefill remote block size (16 vs 32), or
  • fail during startup/health check with a clear unsupported-configuration error.

It should not report the server as healthy and then fail per-request NIXL handshakes.

Actual behavior

At request time, decode workers fail NIXL handshake setup:

NIXL transfer failure: handshake_setup_failed
AssertionError: Local block size 16 is not divisible by remote block size 32 or vice versa.

The compatibility hash check passes before this:

NIXL compatibility check passed
Transfer plan: TransferTopology(tp_ratio=-4, num_kv_heads=2, local_tp=1, remote_tp=4, local_rank=0, remote_block_len=32768)

Then requests fail KV loading:

NIXL transfer failure: handshake_failed
Skipping KV post-processing for failed request ...

Observed correctness impact

Running GSM8K against the failing configuration returned invalid correctness:

Accuracy: 0.031
Invalid responses: 0.000
Total output tokens: 8192

This is not a meaningful model-quality result because NIXL transfer errors are present in the decode logs.

Log excerpt

(Worker_DP3_EP3 pid=349953) INFO 05-17 20:14:00 [worker.py:555] NIXL compatibility check passed (hash: 63cfb9d5c28916a5ccc703d83c44c6dec2009cdbaf85edbe0f9cec80e5762d81)
(Worker_DP3_EP3 pid=349953) INFO 05-17 20:14:00 [worker.py:1301] Transfer plan: TransferTopology(tp_ratio=-4, num_kv_heads=2, local_tp=1, remote_tp=4, local_rank=0, remote_block_len=32768)
(Worker_DP3_EP3 pid=349953) ERROR 05-17 20:14:00 [worker.py:735] NIXL transfer failure: handshake_setup_failed | Context: {'failure_type': 'handshake_setup_failed', 'request_id': None, 'engine_id': '6ff14cc5-a32c-46c9-80ed-801031f38e11_dp0', 'remote_engine_id': '944cb3df-ccb0-4860-9fae-15ec5c53a9ae'}
Traceback (most recent call last):
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 733, in done_callback
    self._remote_agents[eid] = f.result()
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
  File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/usr/lib/python3.12/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 581, in _nixl_handshake
    remote_agent_name = self.add_remote_agent(
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py", line 1320, in add_remote_agent
    block_size_ratio = transfer_topo.block_size_ratio(nixl_agent_meta.block_size)
  File "/opt/vllm/vllm/distributed/kv_transfer/kv_connector/utils.py", line 519, in block_size_ratio
    assert self.block_size % remote_block_size == 0, (
AssertionError: Local block size 16 is not divisible by remote block size 32 or vice versa.
(Worker_DP3_EP3 pid=349953) WARNING 05-17 20:14:00 [worker.py:1735] Skipping KV post-processing for failed request 5a4c1f4d-e725-40c4-944f-6c0d0584f627-b6ed42d1

Full logs: 170987.zip

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FAQ

Expected behavior

NIXL should either:

  • support the reciprocal block-size case where decode local block size is smaller than prefill remote block size (16 vs 32), or
  • fail during startup/health check with a clear unsupported-configuration error.

It should not report the server as healthy and then fail per-request NIXL handshakes.

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vllm - 💡(How to fix) Fix [Bug]: NIXL disagg fails for Qwen3.5 hybrid model when prefill TP4 and decode DP8 use different physical block sizes