[Bug]: RuntimeError: CUDA error: no kernel image is available for execution on the device using H100 starting RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic #19690
Description
Your current environment
The output of python collect_env.py
INFO 06-16 08:00:07 [__init__.py:244] Automatically detected platform cuda.
Collecting environment information...
==============================
System Info
==============================
OS : AlmaLinux 9.6 (Sage Margay) (x86_64)
GCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.34
==============================
PyTorch Info
==============================
PyTorch version : 2.7.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.9 (main, Feb 4 2025, 00:00:00) [GCC 11.5.0 20240719 (Red Hat 11.5.0-5)] (64-bit runtime)
Python platform : Linux-5.14.0-570.21.1.el9_6.x86_64-x86_64-with-glibc2.34
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H100 PCIe
GPU 1: NVIDIA H100 PCIe
GPU 2: NVIDIA H100 PCIe
Nvidia driver version : 570.148.08
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): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6438Y+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 8
CPU(s) scaling MHz: 96%
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 4000.00
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 cdp_l2 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi 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 ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 128 MiB (64 instances)
L3 cache: 120 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127
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 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 BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.3.14
[pip3] nvidia-cuda-cupti-cu12==12.8.57
[pip3] nvidia-cuda-nvrtc-cu12==12.8.61
[pip3] nvidia-cuda-runtime-cu12==12.8.57
[pip3] nvidia-cudnn-cu12==9.7.1.26
[pip3] nvidia-cufft-cu12==11.3.3.41
[pip3] nvidia-cufile-cu12==1.13.0.11
[pip3] nvidia-curand-cu12==10.3.9.55
[pip3] nvidia-cusolver-cu12==11.7.2.55
[pip3] nvidia-cusparse-cu12==12.5.7.53
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.8.61
[pip3] nvidia-nvtx-cu12==12.8.55
[pip3] pyzmq==27.0.0
[pip3] torch==2.7.0+cu128
[pip3] torchaudio==2.7.0+cu128
[pip3] torchvision==0.22.0+cu128
[pip3] transformers==4.52.4
[pip3] triton==3.3.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.9.1
vLLM Build Flags:
CUDA Archs: 9.0; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NODE SYS NODE 0,2,4,6,8,10 0 N/A
GPU1 NODE X SYS NODE 0,2,4,6,8,10 0 N/A
GPU2 SYS SYS X SYS 1,3,5,7,9,11 1 N/A
NIC0 NODE NODE SYS 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_bond_0
==============================
Environment Variables
==============================
TORCH_CUDA_ARCH_LIST=9.0
NCCL_SOCKET_IFNAME=bond0.704
CUDA_VISIBLE_DEVICES=0,1,2,3
CUDA_VISIBLE_DEVICES=0,1,2,3
VLLM_HOST_IP=10.247.4.53
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
When attempting to run RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic on vllm 0.9.1 (vllm serve --config llama.yaml). Vllm does not start and this appears in the log:
(VllmWorker rank=1 pid=14283) ERROR 06-16 07:53:45 [multiproc_executor.py:527] RuntimeError: CUDA error: no kernel image is available for execution on the device
(VllmWorker rank=1 pid=14283) ERROR 06-16 07:53:45 [multiproc_executor.py:527] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(VllmWorker rank=1 pid=14283) ERROR 06-16 07:53:45 [multiproc_executor.py:527] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(VllmWorker rank=1 pid=14283) ERROR 06-16 07:53:45 [multiproc_executor.py:527] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
This model worked fine under 0.8.5.post1.
lllama.yaml:
model: /opt/llm/models/RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic
served-model-name: llama-4-scout
max-model-len: 65536
tensor_parallel_size: 2
port: 8002
api_key: XXXXXXX
enable_auto_tool_choice: true
tool_call_parser: llama4_pythonic
chat-template: /opt/llm/vllm/conf/tool_chat_template_llama4_pythonic.jinja
disable-log-requests: true
override-generation-config: '{"attn_temperature_tuning": true}'
Environment variables:
CUDA_VISIBLE_DEVICES=0,2
VLLM_DISABLE_COMPILE_CACHE=1
NCCL_SOCKET_IFNAME=bond0.704
GOLO_SOCKET_IFNAME=bond0.704
VLLM_HOST_IP=10.247.4.53
TORCH_CUDA_ARCH_LIST=9.0
The output and stacktraces from vllm are in this gist: https://gist.github.com/camshrun/464d62b3e49df4b5ef8df76c50383c98
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