[Usage]: How to reach 100% GPU Compute Utilization ?

[MohamedAliRashad]

Your current environment

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

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: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov  6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 550.127.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        43 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) Platinum 8468
CPU family:                           6
Model:                                143
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            2
Stepping:                             8
BogoMIPS:                             4200.00
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            4 MiB (128 instances)
L1i cache:                            4 MiB (128 instances)
L2 cache:                             256 MiB (64 instances)
L3 cache:                             32 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-63
NUMA node1 CPU(s):                    64-127
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:        Unknown: No mitigations
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 and seccomp
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 SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnxruntime==1.20.1
[pip3] pytorch-lightning==2.4.0
[pip3] pyzmq==26.2.0
[pip3] sentence-transformers==3.3.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchcrepe==0.0.23
[pip3] torchmetrics==1.6.0
[pip3] torchvision==0.20.1
[pip3] transformers==4.48.0.dev0
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.5
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     PIX     PHB     PHB     PHB     0-63    0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     PHB     PIX     PHB     PHB     0-63    0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     PHB     PHB     PIX     PHB     0-63    0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PIX     0-63    0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    PIX     PHB     PHB     PHB     SYS     SYS     SYS     SYS     64-127  1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    PHB     PIX     PHB     PHB     SYS     SYS     SYS     SYS     64-127  1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    PHB     PHB     PIX     PHB     SYS     SYS     SYS     SYS     64-127  1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      PHB     PHB     PHB     PIX     SYS     SYS     SYS     SYS     64-127  1               N/A
NIC0    SYS     SYS     SYS     SYS     PIX     PHB     PHB     PHB      X      PHB     PHB     PHB     SYS     SYS     SYS     SYS
NIC1    SYS     SYS     SYS     SYS     PHB     PIX     PHB     PHB     PHB      X      PHB     PHB     SYS     SYS     SYS     SYS
NIC2    SYS     SYS     SYS     SYS     PHB     PHB     PIX     PHB     PHB     PHB      X      PHB     SYS     SYS     SYS     SYS
NIC3    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PIX     PHB     PHB     PHB      X      SYS     SYS     SYS     SYS
NIC4    PIX     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PHB     PHB     PHB
NIC5    PHB     PIX     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB      X      PHB     PHB
NIC6    PHB     PHB     PIX     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB      X      PHB
NIC7    PHB     PHB     PHB     PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB      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

LD_LIBRARY_PATH=/home/morashad/.local/lib/python3.10/site-packages/cv2/../../lib64:
CUDA_MODULE_LOADING=LAZY

How would you like to use vllm

I want to run inference of a Command R Plus using 8x H100 and i can't get the GPUs to reach 100% GPU Compute. This is my vllm command:

vllm serve CohereForAI/c4ai-command-r-plus-08-2024 --device cuda --dtype bfloat16 --tensor-parallel-size 8 --gpu-memory-utilization 0.95 --max-num-batched-tokens 262144 --max-num-seqs 1024 --max-model-len 32768 --block-size 32 --swap-space 16 --host 0.0.0.0 --port 8000

I have no idea what i am doing wrong or what is the bottleneck or where should i search for tips and tricks about vllm deployment (I think there is a bottle neck regarding the CPU because htop only shown me 8 cores running at every moment
or a problem with GPU KV cache as i see in the logs 99% utalization of this which is strange because there is a lot of VRAM still there while i am making it clear in the command to use 95% of all the vram memory)

Anyone has any ideas ?

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