[Usage]: how to use run in mixed mode CPU/GPU (device_map="auto")

[osafaimal]

Your current environment

Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-28-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A30
GPU 1: Quadro M2000

Nvidia driver version: 545.23.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture :                          x86_64
Mode(s) opératoire(s) des processeurs : 32-bit, 64-bit
Address sizes:                          46 bits physical, 48 bits virtual
Boutisme :                              Little Endian
Processeur(s) :                         8
Liste de processeur(s) en ligne :       0-7
Identifiant constructeur :              GenuineIntel
Nom de modèle :                         Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz
Famille de processeur :                 6
Modèle :                                63
Thread(s) par cœur :                    2
Cœur(s) par socket :                    4
Socket(s) :                             1
Révision :                              2
Vitesse maximale du processeur en MHz : 3600,0000
Vitesse minimale du processeur en MHz : 1200,0000
BogoMIPS :                              6983.83
Drapaux :                               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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf 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 cpuid_fault epb invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts vnmi md_clear flush_l1d
Virtualisation :                        VT-x
Cache L1d :                             128 KiB (4 instances)
Cache L1i :                             128 KiB (4 instances)
Cache L2 :                              1 MiB (4 instances)
Cache L3 :                              10 MiB (1 instance)
Nœud(s) NUMA :                          1
Nœud NUMA 0 de processeur(s) :          0-7
Vulnerability Gather data sampling:     Not affected
Vulnerability Itlb multihit:            KVM: Mitigation: VMX disabled
Vulnerability L1tf:                     Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:                 Mitigation; PTI
Vulnerability Mmio stale data:          Mitigation; Clear CPU buffers; SMT vulnerable
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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                    Not affected
Vulnerability Tsx async abort:          Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] triton==2.3.0
[pip3] vllm_nccl_cu12==2.18.1.0.4.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     0-7     0               N/A
GPU1    PHB      X      0-7     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

How would you like to use vllm

i want to use LLM models that don't fit on my gpu so i would like to know how i can use vllm to run models in mixed mode CPU/GPU. same as device_map="auto" with transformers.