Your current environment

============================== PyTorch Info

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

============================== Python Environment

Python version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime) Python platform : Linux-6.8.0-106-generic-x86_64-with-glibc2.39

============================== CUDA / GPU Info

Is CUDA available : True CUDA runtime version : 12.8.93 CUDA_MODULE_LOADING set to : GPU models and configuration : GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition Nvidia driver version : 580.126.09 cuDNN version : Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0 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): 128 On-line CPU(s) list: 0-127 Vendor ID: AuthenticAMD Model name: AMD EPYC 9355 32-Core Processor CPU family: 26 Model: 2 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 1 BogoMIPS: 7089.26 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d debug_swap srso_user_kernel_no Virtualization: AMD-V L1d cache: 3 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 64 MiB (64 instances) L3 cache: 512 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-31,64-95 NUMA node1 CPU(s): 32-63,96-127 Vulnerability Gather data sampling: 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 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; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected 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==0.6.6 [pip3] numpy==2.1.2 [pip3] nvidia-cublas-cu12==12.8.4.1 [pip3] nvidia-cuda-cupti-cu12==12.8.90 [pip3] nvidia-cuda-nvrtc-cu12==12.8.93 [pip3] nvidia-cuda-runtime-cu12==12.8.90 [pip3] nvidia-cudnn-cu12==9.10.2.21 [pip3] nvidia-cudnn-frontend==1.18.0 [pip3] nvidia-cufft-cu12==11.3.3.83 [pip3] nvidia-cufile-cu12==1.13.1.3 [pip3] nvidia-curand-cu12==10.3.9.90 [pip3] nvidia-cusolver-cu12==11.7.3.90 [pip3] nvidia-cusparse-cu12==12.5.8.93 [pip3] nvidia-cusparselt-cu12==0.7.1 [pip3] nvidia-cutlass-dsl==4.5.0.dev0 [pip3] nvidia-cutlass-dsl-libs-base==4.5.0.dev0 [pip3] nvidia-ml-py==13.595.45 [pip3] nvidia-nccl-cu12==2.27.5 [pip3] nvidia-nvjitlink-cu12==12.8.93 [pip3] nvidia-nvshmem-cu12==3.4.5 [pip3] nvidia-nvtx-cu12==12.8.90 [pip3] pyzmq==27.1.0 [pip3] torch==2.10.0 [pip3] torch_c_dlpack_ext==0.1.5 [pip3] torchaudio==2.10.0 [pip3] torchvision==0.25.0 [pip3] transformers==5.6.0 [pip3] triton==3.6.0 [conda] Could not collect

============================== vLLM Info

ROCM Version : Could not collect vLLM Version : 0.19.1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled GPU Topology: GPU0 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS SYS 32-63,96-127 1 N/A NIC0 SYS X PIX NIC1 SYS 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

============================== Environment Variables

NVIDIA_VISIBLE_DEVICES=void NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 NCCL_VERSION=2.25.1-1 NVIDIA_DRIVER_CAPABILITIES=compute,utility NVIDIA_PRODUCT_NAME=CUDA CUDA_VERSION=12.8.1 LD_LIBRARY_PATH=/usr/local/cuda/lib64 NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/x86_64-linux-gnu PYTORCH_NVML_BASED_CUDA_CHECK=1 TORCHINDUCTOR_COMPILE_THREADS=1 TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

🐛 Describe the bug

On a RTX PRO 6000 GPU, forcing the FLASHINFER attention backend for Gemma-4 causes vLLM to fail during engine initialization with:

ValueError: Selected backend AttentionBackendEnum.FLASHINFER is not valid for this configuration. Reason: ['head_size not supported']

Without the override, the same model loads and runs correctly with TRITON_ATTN.

This reproduces for:

• the base model: google/gemma-4-31b-it
• quantized FP8 checkpoint
• quantized NVFP4 checkpoint

Gemma-4 config on this model reports:

• head_dim=256
• global_head_dim=512

Minimal repro

from vllm import LLM

LLM(
    model="google/gemma-4-31b-it",
    tokenizer="google/gemma-4-31b-it",
    dtype="bfloat16",
    tensor_parallel_size=1,
    max_model_len=2048,
    gpu_memory_utilization=0.9,
    enforce_eager=True,
    disable_log_stats=True,
    attention_config={"backend": "FLASHINFER"},
)

Observed failure:

Using AttentionBackendEnum.FLASHINFER backend.
ValueError: Selected backend AttentionBackendEnum.FLASHINFER is not valid for this configuration. Reason: ['head_size not supported']

What I expected

Either:

1. FLASHINFER should work for Gemma-4 on this configuration, or
2. vLLM should reject the override earlier or more explicitly for Gemma-4 with a clearer compatibility message.

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