Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
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
==============================
Python Environment
==============================
Python version : 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.15.0-25-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration : GPU 0: NVIDIA GeForce RTX 5090
Nvidia driver version : 580.82.09
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
架构： x86_64
CPU 运行模式： 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
字节序： Little Endian
CPU: 32
在线 CPU 列表： 0-31
厂商 ID： GenuineIntel
型号名称： INTEL(R) XEON(R) GOLD 6526Y
CPU 系列： 6
型号： 207
每个核的线程数： 2
每个座的核数： 16
座： 1
步进： 2
CPU 最大 MHz： 3900.0000
CPU 最小 MHz： 800.0000
BogoMIPS： 5600.00
标记： 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 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 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 avx512_fp16 flush_l1d arch_capabilities
虚拟化： VT-x
L1d 缓存： 768 KiB (16 instances)
L1i 缓存： 512 KiB (16 instances)
L2 缓存： 32 MiB (16 instances)
L3 缓存： 37.5 MiB (1 instance)
NUMA 节点： 1
NUMA 节点0 CPU： 0-31
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: 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
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.4
[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==1.17.0
[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==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.44
[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==0.1.4
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.57.3
[pip3] triton==3.6.0
[conda] flashinfer-python 0.6.4 pypi_0 pypi
[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 1.17.0 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-dsl 4.4.1 pypi_0 pypi
[conda] nvidia-cutlass-dsl-libs-base 4.4.1 pypi_0 pypi
[conda] nvidia-ml-py 13.590.44 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 3.4.5 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] pyzmq 27.1.0 pypi_0 pypi
[conda] torch 2.10.0 pypi_0 pypi
[conda] torch-c-dlpack-ext 0.1.4 pypi_0 pypi
[conda] torchaudio 2.10.0 pypi_0 pypi
[conda] torchvision 0.25.0 pypi_0 pypi
[conda] transformers 4.57.3 pypi_0 pypi
[conda] triton 3.6.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.17.0rc1.dev199+g179547d62.d20260310 (git sha: 179547d62, date: 20260310)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X 0-31 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_HOME=/usr/local/cuda-12.9
CUDA_HOME=/usr/local/cuda-12.9
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_xjy
🐛 Describe the bug
Description
Running Qwen3.5-9B with vLLM on non-SM90 GPUs (e.g. RTX 5090, SM120) causes a Triton out of memory error during the first inference request. The error originates from the Triton autotuner trying to benchmark kernel configurations for GDN (Gated Delta Net) linear attention layers after vLLM has already allocated most GPU memory for KV cache.
Root cause: During V1 profile runs, _forward_core in Qwen3NextGatedDeltaNet returns early when attn_metadata is None (qwen3_next.py#L640-L642), so the Triton-autotuned kernels (solve_tril, chunk_scaled_dot_kkt, etc.) are never invoked during profiling. After profiling, vLLM allocates KV cache using most of the remaining GPU memory. When the first real inference triggers the Triton autotuner, it OOMs because there is insufficient memory left for benchmarking.
This only affects GPUs using the Triton-based forward_native path (non-SM90). SM90 GPUs (H100/H200) use the FlashInfer forward_cuda path which has no Triton autotuner.
Reproduction
from vllm import LLM, SamplingParams
llm = LLM(
model="Qwen/Qwen3.5-9B",
tensor_parallel_size=1,
max_model_len=4096,
enforce_eager=True,
)
prompts = ["Hello, my name is"]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=16)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
print(f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}")Error
ERROR 03-09 21:56:25 [dump_input.py:79] Dumping scheduler output for model execution:
...
Traceback (most recent call last):
File ".../vllm/model_executor/layers/fla/ops/solve_tril.py", line 63, in solve_tril_kernel
...
triton Error [CUDA]: out of memory
...
File ".../vllm/model_executor/layers/fla/ops/chunk.py", in chunk_gated_delta_rule_fwd
A = solve_tril(A, cu_seqlens=cu_seqlens)
File ".../vllm/model_executor/layers/fla/ops/solve_tril.py", in solve_tril
solve_tril_kernelgrid
...
File ".../triton/runtime/autotuner.py", in run
timings = {config: self._bench(*args, config=config, **kwargs)
The OOM occurs inside Triton's autotuner _bench() — it needs temporary GPU memory to benchmark 16 kernel configs (num_warps=[1,2,4,8] × num_stages=[2,3,4,5]), but there is none left after KV cache allocation.
Analysis
The Qwen3.5-9B model has 32 layers: 24 GDN (linear attention) layers + 8 full attention layers. Each GDN layer uses chunk_gated_delta_rule which internally calls Triton kernels decorated with @triton.autotune. On non-SM90 GPUs, these go through the forward_native (Triton) path rather than forward_cuda (FlashInfer).
The V1 engine profile_run() → _dummy_run() does not build attn_metadata (it remains None), causing _forward_core to return early without ever invoking the Triton kernels. The autotuner is therefore never warmed up during profiling when memory is plentiful.
Suggested Fix
Call chunk_gated_delta_rule with small dummy tensors during the profile phase (when attn_metadata is None) to trigger Triton autotuning before KV cache allocation. I will submit a PR.
Before submitting a new issue...
Your current environment
The output of
python collect_env.py🐛 Describe the bug
Description
Running Qwen3.5-9B with vLLM on non-SM90 GPUs (e.g. RTX 5090, SM120) causes a Triton
out of memoryerror during the first inference request. The error originates from the Triton autotuner trying to benchmark kernel configurations for GDN (Gated Delta Net) linear attention layers after vLLM has already allocated most GPU memory for KV cache.Root cause: During V1 profile runs,
_forward_coreinQwen3NextGatedDeltaNetreturns early whenattn_metadata is None(qwen3_next.py#L640-L642), so the Triton-autotuned kernels (solve_tril,chunk_scaled_dot_kkt, etc.) are never invoked during profiling. After profiling, vLLM allocates KV cache using most of the remaining GPU memory. When the first real inference triggers the Triton autotuner, it OOMs because there is insufficient memory left for benchmarking.This only affects GPUs using the Triton-based
forward_nativepath (non-SM90). SM90 GPUs (H100/H200) use the FlashInferforward_cudapath which has no Triton autotuner.Reproduction
Error
ERROR 03-09 21:56:25 [dump_input.py:79] Dumping scheduler output for model execution:
...
Traceback (most recent call last):
File ".../vllm/model_executor/layers/fla/ops/solve_tril.py", line 63, in solve_tril_kernel
...
triton Error [CUDA]: out of memory
...
File ".../vllm/model_executor/layers/fla/ops/chunk.py", in chunk_gated_delta_rule_fwd
A = solve_tril(A, cu_seqlens=cu_seqlens)
File ".../vllm/model_executor/layers/fla/ops/solve_tril.py", in solve_tril
solve_tril_kernelgrid
...
File ".../triton/runtime/autotuner.py", in run
timings = {config: self._bench(*args, config=config, **kwargs)
The OOM occurs inside Triton's autotuner _bench() — it needs temporary GPU memory to benchmark 16 kernel configs (num_warps=[1,2,4,8] × num_stages=[2,3,4,5]), but there is none left after KV cache allocation.
Analysis
The Qwen3.5-9B model has 32 layers: 24 GDN (linear attention) layers + 8 full attention layers. Each GDN layer uses chunk_gated_delta_rule which internally calls Triton kernels decorated with @triton.autotune. On non-SM90 GPUs, these go through the forward_native (Triton) path rather than forward_cuda (FlashInfer).
The V1 engine profile_run() → _dummy_run() does not build attn_metadata (it remains None), causing _forward_core to return early without ever invoking the Triton kernels. The autotuner is therefore never warmed up during profiling when memory is plentiful.
Suggested Fix
Call chunk_gated_delta_rule with small dummy tensors during the profile phase (when attn_metadata is None) to trigger Triton autotuning before KV cache allocation. I will submit a PR.
Before submitting a new issue...