vllm - ✅(Solved) Fix [Bug]: Deepseek-v3 fails on 8xB200 in v0.17.0 (including eager) [1 pull requests, 4 comments, 2 participants]

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vllm-project/vllm#36662Fetched 2026-04-08 00:35:32
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Fix Action

Fix / Workaround

============================== 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

PR fix notes

PR #36551: [torch.compile] Add support for non-contiguous fused RMSNorm + group quant

Description (problem / solution / changelog)

Background

Fused rms_norm + group fp8 quant kernel only supports contiguous inputs. This is an issue in the Deepseek case, because the norm input is a slice of the qkv_lora tensor:

vllm/model_executor/layers/mla.py:134-139:

q_c, kv_lora = qkv_lora.split(
    [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
    dim=-1,
)
q_c = self.q_a_layernorm(q_c)
q = self.q_b_proj(q_c)[0]

Current rms_norm + quant fusion with rms_norm disabled (default) inserts redundant type conversions in between that prevent this error from happening by default. However, with rms_norm enabled, an error occurs (below). With vLLM IR (#32358), these redundant type conversions are gone and so the following error occurs as well.

$ vllm serve deepseek-ai/DeepSeek-V3 -cc.custom_ops+=+rms_norm -tp=8
(Worker pid=1951505) (Worker_TP5 pid=1951505) ERROR 03-10 13:34:42 [multiproc_executor.py:932] RuntimeError: Expected out.is_contiguous() && input.is_contiguous() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)

Changes

Add input_stride arg to allow for padded higher dims for rms_quant input, and add appropriate unit tests. Also add deepseek to E2E fusion tests.

Test Plan

Validated locally, CI, lm_eval

Test Result

$ vllm serve qwen/qwen3-30b-a3b-fp8 
$ lm_eval --model local-completions --model_args pretrained=qwen/qwen3-30b-a3b-fp8,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3 --tasks gsm8k --num_fewshot 5 --batch_size auto --limit 100
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.86±0.0349
strict-match5exact_match0.91±0.0288

lm-eval appears broken for DSv3 (#36662), fix in #36296. Below results include this PR

$ vllm serve deepseek-ai/DeepSeek-V3
$ lm_eval --model local-completions --model_args pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3 --tasks gsm8k --num_fewshot 5 --batch_size auto

#36296:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8

# no deepgemm
local-completions (pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.9462±0.0062
strict-match5exact_match0.9462±0.0062
# with deepgemm
local-completions (pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.95±0.006
strict-match5exact_match0.95±0.006

#36296 + this PR:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8

local-completions (pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.9477±0.0061
strict-match5exact_match0.9477±0.0061
vllm serve deepseek-ai/DeepSeek-V3 -cc.custom_ops+=+rms_norm -tp=8

local-completions (pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.9507±0.006
strict-match5exact_match0.9507±0.006

Just this PR:

TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0.01±0.01
strict-match5exact_match0.00±0.00

Perf

vllm bench serve --dataset-name random --ignore-eos --model=deepseek-ai/DeepSeek-V3 --num-prompts 120 --request-rate 1

#36296 + this PR:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8

============ Serving Benchmark Result ============
Successful requests:                     120       
Failed requests:                         0         
Request rate configured (RPS):           1.00      
Benchmark duration (s):                  121.17    
Total input tokens:                      122760    
Total generated tokens:                  15360     
Request throughput (req/s):              0.99      
Output token throughput (tok/s):         126.77    
Peak output token throughput (tok/s):    462.00    
Peak concurrent requests:                9.00      
Total token throughput (tok/s):          1139.92   
---------------Time to First Token----------------
Mean TTFT (ms):                          106.44    
Median TTFT (ms):                        106.73    
P99 TTFT (ms):                           177.41    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.05     
Median TPOT (ms):                        9.72      
P99 TPOT (ms):                           13.91     
---------------Inter-token Latency----------------
Mean ITL (ms):                           10.05     
Median ITL (ms):                         9.15      
P99 ITL (ms):                            81.27     
==================================================

# Note that +rms_norm slows down rms_norm so perf hit is expected 
(even though fusion benefits from removing the cast that gets inserted when -rms_norm).
# vLLM IR will resolve these kind of issues cleanly
vllm serve deepseek-ai/DeepSeek-V3 -cc.custom_ops+=+rms_norm -tp=8

============ Serving Benchmark Result ============
Successful requests:                     120       
Failed requests:                         0         
Request rate configured (RPS):           1.00      
Benchmark duration (s):                  121.17    
Total input tokens:                      122760    
Total generated tokens:                  15360     
Request throughput (req/s):              0.99      
Output token throughput (tok/s):         126.77    
Peak output token throughput (tok/s):    452.00    
Peak concurrent requests:                9.00      
Total token throughput (tok/s):          1139.93   
---------------Time to First Token----------------
Mean TTFT (ms):                          107.80    
Median TTFT (ms):                        105.39    
P99 TTFT (ms):                           182.84    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.16     
Median TPOT (ms):                        9.82      
P99 TPOT (ms):                           14.03     
---------------Inter-token Latency----------------
Mean ITL (ms):                           10.16     
Median ITL (ms):                         9.27      
P99 ITL (ms):                            80.03     
==================================================

#36296:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8

============ Serving Benchmark Result ============
Successful requests:                     120       
Failed requests:                         0         
Request rate configured (RPS):           1.00      
Benchmark duration (s):                  121.12    
Total input tokens:                      122760    
Total generated tokens:                  15360     
Request throughput (req/s):              0.99      
Output token throughput (tok/s):         126.82    
Peak output token throughput (tok/s):    468.00    
Peak concurrent requests:                9.00      
Total token throughput (tok/s):          1140.36   
---------------Time to First Token----------------
Mean TTFT (ms):                          192.34    
Median TTFT (ms):                        112.36    
P99 TTFT (ms):                           2195.44   
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          10.36     
Median TPOT (ms):                        9.57      
P99 TPOT (ms):                           24.47     
---------------Inter-token Latency----------------
Mean ITL (ms):                           10.36     
Median ITL (ms):                         8.88      
P99 ITL (ms):                            84.25     
==================================================

# With deepgemm (vllm docker image)

============ Serving Benchmark Result ============
Successful requests:                     120       
Failed requests:                         0         
Request rate configured (RPS):           1.00      
Benchmark duration (s):                  121.09    
Total input tokens:                      122760    
Total generated tokens:                  15360     
Request throughput (req/s):              0.99      
Output token throughput (tok/s):         126.85    
Peak output token throughput (tok/s):    454.00    
Peak concurrent requests:                9.00      
Total token throughput (tok/s):          1140.63   
---------------Time to First Token----------------
Mean TTFT (ms):                          106.40    
Median TTFT (ms):                        107.15    
P99 TTFT (ms):                           182.38    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          9.52      
Median TPOT (ms):                        9.21      
P99 TPOT (ms):                           13.34     
---------------Inter-token Latency----------------
Mean ITL (ms):                           9.52      
Median ITL (ms):                         8.66      
P99 ITL (ms):                            84.35     
==================================================

Changed files

  • .buildkite/test_areas/compile.yaml (modified, +9/-9)
  • csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu (modified, +45/-24)
  • csrc/quantization/fused_kernels/layernorm_utils.cuh (modified, +44/-27)
  • tests/compile/fusions_e2e/conftest.py (modified, +10/-0)
  • tests/compile/fusions_e2e/models.py (modified, +36/-0)
  • tests/compile/fusions_e2e/test_tp1_quant.py (modified, +13/-8)
  • tests/compile/fusions_e2e/test_tp2_ar_rms.py (modified, +9/-4)
  • tests/kernels/core/test_fused_quant_layernorm.py (modified, +51/-13)
  • vllm/_custom_ops.py (modified, +2/-2)

Code Example

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+cu129
Is debug build               : False
CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar  4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.8.0-85-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 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        : 580.126.09
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] flashinfer-python==0.6.4
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[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==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu129
[pip3] torchvision==0.25.0+cu129
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.17.0
vLLM Build Flags:
  CUDA Archs: 7.0 7.5 8.0 8.9 9.0 10.0 12.0; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6NIC7    NIC8    NIC9    NIC10   NIC11   CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    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    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    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    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     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     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     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     NODENODE    NODE    NODE    NODE    PXB     56-111,168-223  1               N/A
NIC0    PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      PIX     NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC2    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    PIX      X      NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC3    NODE    PXB     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC4    NODE    NODE    PXB     NODE    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE     X      NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC5    NODE    NODE    NODE    PXB     SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    NODE     X      SYSSYS      SYS     SYS     SYS     SYS                             
NIC6    SYS     SYS     SYS     SYS     PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS      X NODE     NODE    NODE    NODE    NODE                            
NIC7    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODE X      PIX     NODE    NODE    NODE                            
NIC8    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODEPIX      X      NODE    NODE    NODE                            
NIC9    SYS     SYS     SYS     SYS     NODE    PXB     NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODENODE    NODE     X      NODE    NODE                            
NIC10   SYS     SYS     SYS     SYS     NODE    NODE    PXB     NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODENODE    NODE    NODE     X      NODE                            
NIC11   SYS     SYS     SYS     SYS     NODE    NODE    NODE    PXB     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_4
  NIC1: mlx5_5
  NIC2: mlx5_6
  NIC3: mlx5_7
  NIC4: mlx5_8
  NIC5: mlx5_9
  NIC6: mlx5_10
  NIC7: mlx5_11
  NIC8: mlx5_12
  NIC9: mlx5_13
  NIC10: mlx5_14
  NIC11: mlx5_15

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.9 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 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=7.0 7.5 8.0 8.9 9.0 10.0 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
VLLM_WORKER_MULTIPROC_METHOD=spawn

---

$ vllm serve deepseek-ai/DeepSeek-V3 -tp=8

# Also fails in eager
$ vllm serve deepseek-ai/DeepSeek-V3 -tp=8 --enforce-eager

# TODO checking dp-ep
$ vllm serve deepseek-ai/DeepSeek-V3 -dp=8 --enable-expert-parallel --enforce-eager

# eval command
lm_eval --model local-completions --model_args pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3 --tasks gsm8k --num_fewshot 5 --batch_size auto --limit 100
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
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+cu129
Is debug build               : False
CUDA used to build PyTorch   : 12.9
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar  4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.8.0-85-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 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        : 580.126.09
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] flashinfer-python==0.6.4
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[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==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu129
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu129
[pip3] torchvision==0.25.0+cu129
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.17.0
vLLM Build Flags:
  CUDA Archs: 7.0 7.5 8.0 8.9 9.0 10.0 12.0; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6NIC7    NIC8    NIC9    NIC10   NIC11   CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    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    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    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    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     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     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     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     NODENODE    NODE    NODE    NODE    PXB     56-111,168-223  1               N/A
NIC0    PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC1    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      PIX     NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC2    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    PIX      X      NODE    NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC3    NODE    PXB     NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      NODE    NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC4    NODE    NODE    PXB     NODE    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE     X      NODE    SYSSYS      SYS     SYS     SYS     SYS                             
NIC5    NODE    NODE    NODE    PXB     SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    NODE     X      SYSSYS      SYS     SYS     SYS     SYS                             
NIC6    SYS     SYS     SYS     SYS     PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS      X NODE     NODE    NODE    NODE    NODE                            
NIC7    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODE X      PIX     NODE    NODE    NODE                            
NIC8    SYS     SYS     SYS     SYS     NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODEPIX      X      NODE    NODE    NODE                            
NIC9    SYS     SYS     SYS     SYS     NODE    PXB     NODE    NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODENODE    NODE     X      NODE    NODE                            
NIC10   SYS     SYS     SYS     SYS     NODE    NODE    PXB     NODE    SYS     SYS     SYS     SYS     SYS     SYS     NODENODE    NODE    NODE     X      NODE                            
NIC11   SYS     SYS     SYS     SYS     NODE    NODE    NODE    PXB     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_4
  NIC1: mlx5_5
  NIC2: mlx5_6
  NIC3: mlx5_7
  NIC4: mlx5_8
  NIC5: mlx5_9
  NIC6: mlx5_10
  NIC7: mlx5_11
  NIC8: mlx5_12
  NIC9: mlx5_13
  NIC10: mlx5_14
  NIC11: mlx5_15

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.9 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 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571
TORCH_CUDA_ARCH_LIST=7.0 7.5 8.0 8.9 9.0 10.0 12.0
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.9.1
VLLM_ENABLE_CUDA_COMPATIBILITY=0
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
VLLM_WORKER_MULTIPROC_METHOD=spawn
</details>

🐛 Describe the bug

Reproduced inside the vllm/vllm-openai:0.17.0 docker image.

$ vllm serve deepseek-ai/DeepSeek-V3 -tp=8

# Also fails in eager
$ vllm serve deepseek-ai/DeepSeek-V3 -tp=8 --enforce-eager

# TODO checking dp-ep
$ vllm serve deepseek-ai/DeepSeek-V3 -dp=8 --enable-expert-parallel --enforce-eager

# eval command
lm_eval --model local-completions --model_args pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3 --tasks gsm8k --num_fewshot 5 --batch_size auto --limit 100
TasksVersionFiltern-shotMetricValueStderr
gsm8k3flexible-extract5exact_match0±0
strict-match5exact_match0±0

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

Fix Plan

The issue seems to be related to the vllm serve command failing with a specific model. To fix this, we can try the following steps:

  • Update the TORCH_CUDA_ARCH_LIST environment variable to include the correct CUDA architecture for the NVIDIA B200 GPU.
  • Set the NVIDIA_VISIBLE_DEVICES environment variable to specify the correct GPU device.
  • Verify that the vllm serve command is using the correct PyTorch version and CUDA version.

Example code to update the environment variables:

import os

# Update TORCH_CUDA_ARCH_LIST
os.environ['TORCH_CUDA_ARCH_LIST'] = '7.0 7.5 8.0 8.9 9.0 10.0 12.0'

# Set NVIDIA_VISIBLE_DEVICES
os.environ['NVIDIA_VISIBLE_DEVICES'] = '0'  # Specify the correct GPU device

Alternatively, you can also update the vllm serve command to include the --cuda option to specify the correct CUDA device:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8 --cuda 0

Verification

To verify that the fix worked, you can run the vllm serve command again and check the output for any errors. You can also use the lm_eval command to evaluate the model and check the results.

Example command to verify the fix:

vllm serve deepseek-ai/DeepSeek-V3 -tp=8
lm_eval --model local-completions --model_args pretrained=deepseek-ai/DeepSeek-V3,base_url=http://0.0.0.0:8000/v1/completions,num_concurrent=50,max_retries=3 --tasks gsm8k --num_fewshot 5 --batch_size auto --limit 100

Extra Tips

  • Make sure to check the vllm documentation for any specific requirements or recommendations for running the model on NVIDIA B200 GPUs.
  • If you are still experiencing issues, try updating the PyTorch and CUDA versions to the latest available.
  • You can also try running the model on a different GPU device or with a different batch size to see if it makes a difference.

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vllm - ✅(Solved) Fix [Bug]: Deepseek-v3 fails on 8xB200 in v0.17.0 (including eager) [1 pull requests, 4 comments, 2 participants]