[Bug]: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 vllm bench throughput regression on 2.9 RC on B200

[huydhn]

Your current environment

The env comes from public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:e7064b4dc8e0a7ed633de52fe69871591fdb6cc2

The output of python collect_env.py

==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : version 4.1.0
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

==============================
      Python Environment
==============================
Python version               : 3.12.11 (main, Jun  4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.8.0-83-generic-x86_64-with-glibc2.35

==============================
       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 B200
GPU 1: NVIDIA B200
GPU 2: NVIDIA B200
GPU 3: NVIDIA B200
GPU 4: NVIDIA B200
GPU 5: NVIDIA B200
GPU 6: NVIDIA B200
GPU 7: NVIDIA B200

Nvidia driver version        : 575.57.08
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:                        52 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               224
On-line CPU(s) list:                  0-223
Vendor ID:                            GenuineIntel
Model name:                           INTEL(R) XEON(R) PLATINUM 8570
CPU family:                           6
Model:                                207
Thread(s) per core:                   2
Core(s) per socket:                   56
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 intel_ppin 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 user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req 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:                            5.3 MiB (112 instances)
L1i cache:                            3.5 MiB (112 instances)
L2 cache:                             224 MiB (112 instances)
L3 cache:                             600 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-55,112-167
NUMA node1 CPU(s):                    56-111,168-223
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] efficientnet_pytorch==0.7.1
[pip3] flashinfer-python==0.3.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[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==1.14.1
[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-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] open_clip_torch==2.32.0
[pip3] pynvml==13.0.1
[pip3] pytorch-lightning==2.5.2
[pip3] pyzmq==27.1.0
[pip3] segmentation_models_pytorch==0.4.0
[pip3] sentence-transformers==3.2.1
[pip3] terratorch==1.0.2
[pip3] torch==2.9.0+cu128
[pip3] torchaudio==2.9.0+cu128
[pip3] torchgeo==0.7.0
[pip3] torchmetrics==1.7.4
[pip3] torchvision==0.24.0+cu128
[pip3] transformers==4.56.2
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.5.0
[pip3] tritonclient==2.51.0
[pip3] vector-quantize-pytorch==1.21.2
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.11.0rc2.dev208+ge7064b4dc (git sha: e7064b4dc)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	NIC9	NIC10	NIC11	NIC12	NIC13	NIC14	NIC15	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	PXB	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	NODE	NODE	NODE	PXB	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	PXB	NODE	SYSSYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	PXB	SYSSYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PXBNODE	NODE	NODE	NODE	NODE	56-111,168-223	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	PXB	NODE	NODE	56-111,168-223	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	NODE	PXB	NODE	56-111,168-223	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	NODE	NODE	PXB	56-111,168-223	1		N/A
NIC0	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	 X 	PIX	PIX	PIX	NODE	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC1	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	PIX	 X 	PIX	PIX	NODE	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC2	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	PIX	PIX	 X 	PIX	NODE	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC3	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	PIX	PIX	PIX	 X 	NODE	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC4	PXB	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	 X 	NODE	NODE	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC5	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	 X 	PIX	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC6	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	PIX	 X 	NODE	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC7	NODE	PXB	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	NODE	NODE	 X 	NODE	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC8	NODE	NODE	PXB	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	 X 	NODE	SYSSYS	SYS	SYS	SYS	SYS
NIC9	NODE	NODE	NODE	PXB	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	NODE	 X 	SYSSYS	SYS	SYS	SYS	SYS
NIC10	SYS	SYS	SYS	SYS	PXB	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 X NODE	NODE	NODE	NODE	NODE
NIC11	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODE X 	PIX	NODE	NODE	NODE
NIC12	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODEPIX	 X 	NODE	NODE	NODE
NIC13	SYS	SYS	SYS	SYS	NODE	PXB	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	 X 	NODE	NODE
NIC14	SYS	SYS	SYS	SYS	NODE	NODE	PXB	NODE	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	NODE	 X 	NODE
NIC15	SYS	SYS	SYS	SYS	NODE	NODE	NODE	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	NODENODE	NODE	NODE	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
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9
  NIC10: mlx5_10
  NIC11: mlx5_11
  NIC12: mlx5_12
  NIC13: mlx5_13
  NIC14: mlx5_14
  NIC15: mlx5_15

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
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=all
NVIDIA_PRODUCT_NAME=CUDA
CUDA_VERSION=12.8.1
LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

🐛 Describe the bug

When benchmark PyTorch 2.9.0 RC from #24994 against the current 2.8.0 baseline from vLLM main branch, I notice that there is throughput regression for meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 where the request/s and total token/s drop more than 40%. This doesn't seem like a fluke because I have consistently seen this issue in the last 2 RCs and several rebases on #24994.

The vllm bench throughput configuration for meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 can be found at https://github.com/pytorch/pytorch-integration-testing/blob/main/vllm-benchmarks/benchmarks/cuda/throughput-tests.json#L47-L58:

{
    "test_name": "throughput_llama4_maverick_fp8_tp8",
    "parameters": {
        "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
        "tensor_parallel_size": 8,
        "load_format": "dummy",
        "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
        "num_prompts": 200,
        "backend": "vllm",
        "max_model_len": 8192
    }
}

Or the vllm bench throughput command:

vllm bench throughput \
    --output-json results//throughput_llama4_maverick_fp8_tp8.json \
    --model meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 \
    --tensor-parallel-size 8 \
    --load-format dummy \
    --dataset ./ShareGPT_V3_unfiltered_cleaned_split.json \
    --num-prompts 200 \
    --backend vllm \
    --max-model-len 8192

This also run with the default config from vLLM, which means that the new standalone compile or cudagraph partition features are off by default. cc @zou3519 just in case

Also, this only happens with meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8. Other models are ok.

cc @mgoin @zou3519 @houseroad

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[huydhn]

Output from vllm bench throughput command running with 2.8.0 vs 2.9.0 final RC

[huydhn]

Interestingly, looking back at the historical data in the last 3 months, there has been a steady improvement in throughput perf for meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8. It seems that 2.9 release negates something and reverts the performance back to past values

[atalman]

HI @huydhn if this issue exist since: This doesn't seem like a fluke because I have consistently seen this issue in the last 2 RCs and several rebases on #24994 - why the issue was not created earlier ?

[zou3519]

@huydhn I'm getting the following after running the vllm bench command:

  File "/home/rzou/dev/rc/vllm-rc/vllm/multimodal/parse.py", line 529, in parse_mm_data
    for k, v in mm_data.items():
                ^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'items'

am I missing something in the setup?

[zou3519]

Also I have not been able to get 8000 token/s on a B200 for PyTorch 2.8 (I'm getting more like 4000 token/s, after working around the error above), so.. is anyone else able to reproduce the PyTorch 2.8 numbers locally? @BoyuanFeng @ProExpertProg

[huydhn]

@huydhn I'm getting the following after running the vllm bench command:

  File "/home/rzou/dev/rc/vllm-rc/vllm/multimodal/parse.py", line 529, in parse_mm_data
    for k, v in mm_data.items():
                ^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'items'

am I missing something in the setup?

I haven't seen this before, but want to check that you are building out of the test PR #24994 for 2.9

[huydhn]

HI @huydhn if this issue exist since: This doesn't seem like a fluke because I have consistently seen this issue in the last 2 RCs and several rebases on #24994 - why the issue was not created earlier ?

The issue started to surface on the next to last PyTorch RC on Sep 30th, but there were other issues on cudagraphs partition and standalone compile at that time that obfuscated it. In addition, vLLM trunk build was completed broken for a couple of days last week because of #26065 (an unfortunate event that we are still on focal). By the time, I can address them, the final RC was out (last Friday), so it's prudent to rerun the benchmark e2e, and here we are with this issue confirmed. Rebase and rebuild vLLM on CI is the most time consuming part in each iteration

[ProExpertProg]

@zou3519 do you have flashinfer installed? That's the only thing I can think of that might be missing

[huydhn]

cc @yeqcharlotte

[zou3519]

I do have flashinfer installed. I think my B200 machine is having a bad day (every couple of days the perf just mysteriously drops). I'm attempting to repro on a H100 machine at the moment

[huydhn]

@huydhn I'm getting the following after running the vllm bench command:

  File "/home/rzou/dev/rc/vllm-rc/vllm/multimodal/parse.py", line 529, in parse_mm_data
    for k, v in mm_data.items():
                ^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'items'

am I missing something in the setup?

Just FYI, I can see the same issue on the recent benchmark run https://github.com/pytorch/pytorch-integration-testing/actions/runs/18298001900/job/52100270247#step:16:8131. Just by looking at the error, I would guess this #26240 looks related