vllm - 💡(How to fix) Fix [Bug]: v0.19.1 CUDA illegal memory access with --kv-cache-dtype fp8 + EP + EAGLE3 under concurrent load (Kimi-K2.5) - distinct from #40259 [1 participants]

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vllm-project/vllm#40435Fetched 2026-04-22 07:45:40
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Error Message

File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/attention/mla_attention.py", line 1733, in build common_attn_metadata.compute_num_computed_tokens().cpu() torch.AcceleratorError: CUDA error: an illegal memory access was encountered

Fix Action

Workaround

Remove --kv-cache-dtype fp8 to use default precision (bf16/fp16).

Code Example

(base) user1@user1:/ssd1/tmp$ python3 collect_env.py
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       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
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.13.9 | packaged by Anaconda, Inc. | (main, Oct 21 2025, 19:16:10) [GCC 11.2.0] (64-bit runtime)
Python platform              : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.6.85
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e
GPU 4: NVIDIA H20-3e
GPU 5: NVIDIA H20-3e
GPU 6: NVIDIA H20-3e
GPU 7: NVIDIA H20-3e

Nvidia driver version        : 570.86.15
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:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         INTEL(R) XEON(R) PLATINUM 8558
CPU family:                         6
Model:                              207
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           2
CPU max MHz:                        4000.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4200.00
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 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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 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:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           520 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
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==1.16.0
[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==4.2.0.0
[pip3] nvidia-cutlass-dsl==4.3.2
[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] torchaudio==2.9.0
[pip3] torchvision==0.24.0
[pip3] transformers==4.57.3
[pip3] triton==3.5.0
[conda] cuda-cccl_linux-64                   12.6.77          0                   nvidia
[conda] cuda-command-line-tools              12.6.3           0                   nvidia
[conda] cuda-compiler                        12.6.3           0                   nvidia
[conda] cuda-crt-dev_linux-64                12.6.85          0                   nvidia
[conda] cuda-crt-tools                       12.6.85          0                   nvidia
[conda] cuda-cudart                          12.6.77          0                   nvidia
[conda] cuda-cudart-dev                      12.6.77          0                   nvidia
[conda] cuda-cudart-dev_linux-64             12.6.77          0                   nvidia
[conda] cuda-cudart-static                   12.6.77          0                   nvidia
[conda] cuda-cudart-static_linux-64          12.6.77          0                   nvidia
[conda] cuda-cudart_linux-64                 12.6.77          0                   nvidia
[conda] cuda-cuobjdump                       12.6.77          0                   nvidia
[conda] cuda-cupti                           12.6.80          0                   nvidia
[conda] cuda-cupti-dev                       12.6.80          0                   nvidia
[conda] cuda-cuxxfilt                        12.6.77          0                   nvidia
[conda] cuda-driver-dev                      12.6.77          0                   nvidia
[conda] cuda-driver-dev_linux-64             12.6.77          0                   nvidia
[conda] cuda-gdb                             12.6.77          0                   nvidia
[conda] cuda-libraries                       12.6.3           0                   nvidia
[conda] cuda-libraries-dev                   12.6.3           0                   nvidia
[conda] cuda-nsight                          12.6.77          0                   nvidia
[conda] cuda-nvcc                            12.6.85          0                   nvidia
[conda] cuda-nvcc-dev_linux-64               12.6.85          0                   nvidia
[conda] cuda-nvcc-impl                       12.6.85          0                   nvidia
[conda] cuda-nvcc-tools                      12.6.85          0                   nvidia
[conda] cuda-nvcc_linux-64                   12.6.85          0                   nvidia
[conda] cuda-nvdisasm                        12.6.77          0                   nvidia
[conda] cuda-nvml-dev                        12.6.77          2                   nvidia
[conda] cuda-nvprof                          12.6.80          0                   nvidia
[conda] cuda-nvprune                         12.6.77          0                   nvidia
[conda] cuda-nvrtc                           12.6.85          0                   nvidia
[conda] cuda-nvrtc-dev                       12.6.85          0                   nvidia
[conda] cuda-nvtx                            12.6.77          0                   nvidia
[conda] cuda-nvvm-dev_linux-64               12.6.85          0                   nvidia
[conda] cuda-nvvm-impl                       12.6.85          0                   nvidia
[conda] cuda-nvvm-tools                      12.6.85          0                   nvidia
[conda] cuda-nvvp                            12.6.80          0                   nvidia
[conda] cuda-opencl                          12.6.77          0                   nvidia
[conda] cuda-opencl-dev                      12.6.77          0                   nvidia
[conda] cuda-profiler-api                    12.6.77          0                   nvidia
[conda] cuda-sanitizer-api                   12.6.77          0                   nvidia
[conda] cuda-toolkit                         12.6.3           0                   nvidia
[conda] cuda-tools                           12.6.3           0                   nvidia
[conda] cuda-visual-tools                    12.6.3           0                   nvidia
[conda] flashinfer-python                    0.5.3            pypi_0              pypi
[conda] gds-tools                            1.11.1.6         0                   nvidia
[conda] libcublas                            12.6.4.1         0                   nvidia
[conda] libcublas-dev                        12.6.4.1         0                   nvidia
[conda] libcufft                             11.3.0.4         0                   nvidia
[conda] libcufft-dev                         11.3.0.4         0                   nvidia
[conda] libcufile                            1.11.1.6         0                   nvidia
[conda] libcufile-dev                        1.11.1.6         0                   nvidia
[conda] libcurand                            10.3.7.77        0                   nvidia
[conda] libcurand-dev                        10.3.7.77        0                   nvidia
[conda] libcusolver                          11.7.1.2         0                   nvidia
[conda] libcusolver-dev                      11.7.1.2         0                   nvidia
[conda] libcusparse                          12.5.4.2         0                   nvidia
[conda] libcusparse-dev                      12.5.4.2         0                   nvidia
[conda] libnpp                               12.2.5.30        0                   nvidia
[conda] libnpp-dev                           12.2.5.30        0                   nvidia
[conda] libnvfatbin                          12.6.77          0                   nvidia
[conda] libnvfatbin-dev                      12.6.77          0                   nvidia
[conda] libnvjitlink                         12.6.85          0                   nvidia
[conda] libnvjitlink-dev                     12.6.85          0                   nvidia
[conda] libnvjpeg                            12.3.1.117       0                   nvidia
[conda] libnvjpeg-dev                        12.3.1.117       0                   nvidia
[conda] nsight-compute                       2024.3.2.3       0                   nvidia
[conda] numpy                                2.2.6            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                1.16.0           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                       4.2.0.0          pypi_0              pypi
[conda] nvidia-cutlass-dsl                   4.3.2            pypi_0              pypi
[conda] nvidia-ml-py                         13.580.82        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                  3.3.20           pypi_0              pypi
[conda] nvidia-nvtx-cu12                     12.8.90          pypi_0              pypi
[conda] pyzmq                                27.1.0           pypi_0              pypi
[conda] torch                                2.9.0            pypi_0              pypi
[conda] torchaudio                           2.9.0            pypi_0              pypi
[conda] torchvision                          0.24.0           pypi_0              pypi
[conda] transformers                         4.57.3           pypi_0              pypi
[conda] triton                               3.5.0            pypi_0              pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.12.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS0-47,96-143      0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS0-47,96-143      0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    NODE    PIX     SYS     SYS0-47,96-143      0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    NODE    PIX     SYS     SYS0-47,96-143      0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     SYS     SYS     PIX     NODE48-95,144-191   1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     SYS     SYS     PIX     NODE48-95,144-191   1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    PIX48-95,144-191    1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    PIX48-95,144-191    1               N/A
NIC0    PIX     PIX     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    SYS     SYS
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      PIX     NODE    SYS     SYS
NIC2    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    PIX      X      NODE    SYS     SYS
NIC3    NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      SYS     SYS
NIC4    SYS     SYS     SYS     SYS     PIX     PIX     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE
NIC5    SYS     SYS     SYS     SYS     NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS     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

==============================
     Environment Variables
==============================
TORCHINDUCTOR_CACHE_DIR=/ssd2/cache/torchinductor/user1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

---

docker run --rm -it \
    --gpus all \
    --ipc=host \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -p 8000:8000 \
    -v /ssd1:/model_files \
    --shm-size=150gb \
    vllm/vllm-openai:v0.19.1 \
    --model /model_files/Kimi-K2.5 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \
    --enable-ep-weight-filter \
    --compilation-config '{"pass_config": {"fuse_allreduce_rms": true}}' \
    --kv-cache-dtype fp8 \
    --speculative-config '{"model": "/model_files/Kimi-K2.5-Eagle3", "method": "eagle3", "num_speculative_tokens": 2}' \
    --gpu-memory-utilization 0.85 \
    --max-num-batched-tokens 4096 \
    --mm-encoder-tp-mode data \
    --enable-chunked-prefill \
    --max-num-seqs 20 \
    --enable-prefix-caching \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code

---

vllm bench serve \
    --host localhost \
    --port 8000 \
    --endpoint /v1/chat/completions \
    --model /model_files/h20/Kimi-K2.5 \
    --tokenizer /ssd1/Kimi-K2.5 \
    --backend openai-chat \
    --dataset-name random \
    --num-prompts 120 \
    --request-rate 20 \
    --max-concurrency 20 \
    --save-result \
    --result-dir "./log/" \
    --trust-remote-code

---

# Identical server command but WITHOUT: --kv-cache-dtype fp8
# Same benchmark: 120/120 successful, 0 failed

---

============ Serving Benchmark Result ============
Successful requests:                     120
Failed requests:                         0
Maximum request concurrency:             20
Request rate configured (RPS):           20.00
Benchmark duration (s):                  38.46
Total input tokens:                      122880
Total generated tokens:                  15360
...

---

File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/attention/mla_attention.py", line 1733, in build
    common_attn_metadata.compute_num_computed_tokens().cpu()
torch.AcceleratorError: CUDA error: an illegal memory access was encountered
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
(base) user1@user1:/ssd1/tmp$ python3 collect_env.py
Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       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
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.13.9 | packaged by Anaconda, Inc. | (main, Oct 21 2025, 19:16:10) [GCC 11.2.0] (64-bit runtime)
Python platform              : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.6.85
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: NVIDIA H20-3e
GPU 1: NVIDIA H20-3e
GPU 2: NVIDIA H20-3e
GPU 3: NVIDIA H20-3e
GPU 4: NVIDIA H20-3e
GPU 5: NVIDIA H20-3e
GPU 6: NVIDIA H20-3e
GPU 7: NVIDIA H20-3e

Nvidia driver version        : 570.86.15
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:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         INTEL(R) XEON(R) PLATINUM 8558
CPU family:                         6
Model:                              207
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           2
CPU max MHz:                        4000.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4200.00
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 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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 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:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           520 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
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==1.16.0
[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==4.2.0.0
[pip3] nvidia-cutlass-dsl==4.3.2
[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] torchaudio==2.9.0
[pip3] torchvision==0.24.0
[pip3] transformers==4.57.3
[pip3] triton==3.5.0
[conda] cuda-cccl_linux-64                   12.6.77          0                   nvidia
[conda] cuda-command-line-tools              12.6.3           0                   nvidia
[conda] cuda-compiler                        12.6.3           0                   nvidia
[conda] cuda-crt-dev_linux-64                12.6.85          0                   nvidia
[conda] cuda-crt-tools                       12.6.85          0                   nvidia
[conda] cuda-cudart                          12.6.77          0                   nvidia
[conda] cuda-cudart-dev                      12.6.77          0                   nvidia
[conda] cuda-cudart-dev_linux-64             12.6.77          0                   nvidia
[conda] cuda-cudart-static                   12.6.77          0                   nvidia
[conda] cuda-cudart-static_linux-64          12.6.77          0                   nvidia
[conda] cuda-cudart_linux-64                 12.6.77          0                   nvidia
[conda] cuda-cuobjdump                       12.6.77          0                   nvidia
[conda] cuda-cupti                           12.6.80          0                   nvidia
[conda] cuda-cupti-dev                       12.6.80          0                   nvidia
[conda] cuda-cuxxfilt                        12.6.77          0                   nvidia
[conda] cuda-driver-dev                      12.6.77          0                   nvidia
[conda] cuda-driver-dev_linux-64             12.6.77          0                   nvidia
[conda] cuda-gdb                             12.6.77          0                   nvidia
[conda] cuda-libraries                       12.6.3           0                   nvidia
[conda] cuda-libraries-dev                   12.6.3           0                   nvidia
[conda] cuda-nsight                          12.6.77          0                   nvidia
[conda] cuda-nvcc                            12.6.85          0                   nvidia
[conda] cuda-nvcc-dev_linux-64               12.6.85          0                   nvidia
[conda] cuda-nvcc-impl                       12.6.85          0                   nvidia
[conda] cuda-nvcc-tools                      12.6.85          0                   nvidia
[conda] cuda-nvcc_linux-64                   12.6.85          0                   nvidia
[conda] cuda-nvdisasm                        12.6.77          0                   nvidia
[conda] cuda-nvml-dev                        12.6.77          2                   nvidia
[conda] cuda-nvprof                          12.6.80          0                   nvidia
[conda] cuda-nvprune                         12.6.77          0                   nvidia
[conda] cuda-nvrtc                           12.6.85          0                   nvidia
[conda] cuda-nvrtc-dev                       12.6.85          0                   nvidia
[conda] cuda-nvtx                            12.6.77          0                   nvidia
[conda] cuda-nvvm-dev_linux-64               12.6.85          0                   nvidia
[conda] cuda-nvvm-impl                       12.6.85          0                   nvidia
[conda] cuda-nvvm-tools                      12.6.85          0                   nvidia
[conda] cuda-nvvp                            12.6.80          0                   nvidia
[conda] cuda-opencl                          12.6.77          0                   nvidia
[conda] cuda-opencl-dev                      12.6.77          0                   nvidia
[conda] cuda-profiler-api                    12.6.77          0                   nvidia
[conda] cuda-sanitizer-api                   12.6.77          0                   nvidia
[conda] cuda-toolkit                         12.6.3           0                   nvidia
[conda] cuda-tools                           12.6.3           0                   nvidia
[conda] cuda-visual-tools                    12.6.3           0                   nvidia
[conda] flashinfer-python                    0.5.3            pypi_0              pypi
[conda] gds-tools                            1.11.1.6         0                   nvidia
[conda] libcublas                            12.6.4.1         0                   nvidia
[conda] libcublas-dev                        12.6.4.1         0                   nvidia
[conda] libcufft                             11.3.0.4         0                   nvidia
[conda] libcufft-dev                         11.3.0.4         0                   nvidia
[conda] libcufile                            1.11.1.6         0                   nvidia
[conda] libcufile-dev                        1.11.1.6         0                   nvidia
[conda] libcurand                            10.3.7.77        0                   nvidia
[conda] libcurand-dev                        10.3.7.77        0                   nvidia
[conda] libcusolver                          11.7.1.2         0                   nvidia
[conda] libcusolver-dev                      11.7.1.2         0                   nvidia
[conda] libcusparse                          12.5.4.2         0                   nvidia
[conda] libcusparse-dev                      12.5.4.2         0                   nvidia
[conda] libnpp                               12.2.5.30        0                   nvidia
[conda] libnpp-dev                           12.2.5.30        0                   nvidia
[conda] libnvfatbin                          12.6.77          0                   nvidia
[conda] libnvfatbin-dev                      12.6.77          0                   nvidia
[conda] libnvjitlink                         12.6.85          0                   nvidia
[conda] libnvjitlink-dev                     12.6.85          0                   nvidia
[conda] libnvjpeg                            12.3.1.117       0                   nvidia
[conda] libnvjpeg-dev                        12.3.1.117       0                   nvidia
[conda] nsight-compute                       2024.3.2.3       0                   nvidia
[conda] numpy                                2.2.6            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                1.16.0           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                       4.2.0.0          pypi_0              pypi
[conda] nvidia-cutlass-dsl                   4.3.2            pypi_0              pypi
[conda] nvidia-ml-py                         13.580.82        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                  3.3.20           pypi_0              pypi
[conda] nvidia-nvtx-cu12                     12.8.90          pypi_0              pypi
[conda] pyzmq                                27.1.0           pypi_0              pypi
[conda] torch                                2.9.0            pypi_0              pypi
[conda] torchaudio                           2.9.0            pypi_0              pypi
[conda] torchvision                          0.24.0           pypi_0              pypi
[conda] transformers                         4.57.3           pypi_0              pypi
[conda] triton                               3.5.0            pypi_0              pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.12.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS0-47,96-143      0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS0-47,96-143      0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    NODE    PIX     SYS     SYS0-47,96-143      0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    NODE    PIX     SYS     SYS0-47,96-143      0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     SYS     SYS     PIX     NODE48-95,144-191   1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     SYS     SYS     PIX     NODE48-95,144-191   1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    PIX48-95,144-191    1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    PIX48-95,144-191    1               N/A
NIC0    PIX     PIX     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    SYS     SYS
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      PIX     NODE    SYS     SYS
NIC2    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    PIX      X      NODE    SYS     SYS
NIC3    NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      SYS     SYS
NIC4    SYS     SYS     SYS     SYS     PIX     PIX     NODE    NODE    SYS     SYS     SYS     SYS      X      NODE
NIC5    SYS     SYS     SYS     SYS     NODE    NODE    PIX     PIX     SYS     SYS     SYS     SYS     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

==============================
     Environment Variables
==============================
TORCHINDUCTOR_CACHE_DIR=/ssd2/cache/torchinductor/user1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
</details>

🐛 Describe the bug

Bug Description

When running Kimi-K2.5 with --kv-cache-dtype fp8, vLLM crashes with CUDA error: an illegal memory access was encountered under concurrent request load (20 concurrent requests).

Critical finding: Removing --kv-cache-dtype fp8 (using default auto) results in 100% success rate (120/120 requests) under identical load, while with fp8 only ~25-56% succeed. This confirms a specific FP8 quantization bug, distinct from #40259.

Environment

  • vLLM version: v0.19.1 (docker: vllm/vllm-openai:v0.19.1)
  • Hardware: 8× NVIDIA H20-3e (141GB)
  • Model: moonshot-ai/Kimi-K2.5
  • Draft model: moonshot-ai/Kimi-K2.5-Eagle3 (EAGLE3 speculative decoding)

Reproduction Commands

Server (failing case with fp8):

docker run --rm -it \
    --gpus all \
    --ipc=host \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -p 8000:8000 \
    -v /ssd1:/model_files \
    --shm-size=150gb \
    vllm/vllm-openai:v0.19.1 \
    --model /model_files/Kimi-K2.5 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \
    --enable-ep-weight-filter \
    --compilation-config '{"pass_config": {"fuse_allreduce_rms": true}}' \
    --kv-cache-dtype fp8 \
    --speculative-config '{"model": "/model_files/Kimi-K2.5-Eagle3", "method": "eagle3", "num_speculative_tokens": 2}' \
    --gpu-memory-utilization 0.85 \
    --max-num-batched-tokens 4096 \
    --mm-encoder-tp-mode data \
    --enable-chunked-prefill \
    --max-num-seqs 20 \
    --enable-prefix-caching \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code

Benchmark client (trigger):

vllm bench serve \
    --host localhost \
    --port 8000 \
    --endpoint /v1/chat/completions \
    --model /model_files/h20/Kimi-K2.5 \
    --tokenizer /ssd1/Kimi-K2.5 \
    --backend openai-chat \
    --dataset-name random \
    --num-prompts 120 \
    --request-rate 20 \
    --max-concurrency 20 \
    --save-result \
    --result-dir "./log/" \
    --trust-remote-code

Working case (without fp8):

# Identical server command but WITHOUT: --kv-cache-dtype fp8
# Same benchmark: 120/120 successful, 0 failed

Results Comparison

ConfigurationSuccessfulFailedNotes
With fp8~30-67~90-53Crashes with CUDA illegal memory access
Without fp81200Completes successfully

Success log (without fp8):

============ Serving Benchmark Result ============
Successful requests:                     120
Failed requests:                         0
Maximum request concurrency:             20
Request rate configured (RPS):           20.00
Benchmark duration (s):                  38.46
Total input tokens:                      122880
Total generated tokens:                  15360
...

Error Stack Trace

Crash occurs in MLA attention layer:

File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/attention/mla_attention.py", line 1733, in build
    common_attn_metadata.compute_num_computed_tokens().cpu()
torch.AcceleratorError: CUDA error: an illegal memory access was encountered

Key Findings

  1. 100% reproducible under load: Only affects concurrent processing (20 reqs). Single requests work fine even with fp8
  2. Zero failures without fp8: Removing --kv-cache-dtype fp8 eliminates all crashes
  3. NOT #40259: Persists without any --kv-offloading-size parameter
  4. MLA specific: Crash happens in MLA attention's metadata computation with FP8 memory layout
  5. GPU memory: Usage is low (~1.5%) at crash time, not an OOM issue

Workaround

Remove --kv-cache-dtype fp8 to use default precision (bf16/fp16).

Impact

Blocks production use of FP8 quantization for Kimi-K2.5 with MLA attention under concurrent workloads.

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.

extent analysis

TL;DR

The most likely fix for the CUDA error with FP8 quantization under concurrent request load is to remove the --kv-cache-dtype fp8 flag to use the default precision.

Guidance

  • The error is likely caused by an issue with the MLA attention layer when using FP8 quantization, as indicated by the error stack trace.
  • Removing the --kv-cache-dtype fp8 flag eliminates the crashes, suggesting a problem with the FP8 memory layout.
  • The fact that single requests work fine even with FP8 quantization, but concurrent requests cause crashes, points to a potential issue with memory access or synchronization.
  • To mitigate the issue, removing the --kv-cache-dtype fp8 flag can be used as a temporary workaround.

Example

No code snippet is provided as the issue is related to a specific configuration flag.

Notes

The provided information suggests that the issue is specific to the MLA attention layer and FP8 quantization. Further investigation is needed to determine the root cause of the problem.

Recommendation

Apply the workaround by removing the --kv-cache-dtype fp8 flag to use the default precision, as it has been shown to eliminate the crashes. This will allow for production use of the model under concurrent workloads, although it may not be using the desired FP8 quantization.

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vllm - 💡(How to fix) Fix [Bug]: v0.19.1 CUDA illegal memory access with --kv-cache-dtype fp8 + EP + EAGLE3 under concurrent load (Kimi-K2.5) - distinct from #40259 [1 participants]