vllm - ✅(Solved) Fix [Bug]: chart-helm does not support configuring shared memory (`/dev/shm`) [1 pull requests, 1 participants]

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Root Cause

This is a deployment gap because official vLLM docs explicitly show /dev/shm setup for Kubernetes and explain that it is needed for tensor parallel inference / IPC.

Fix Action

Fix / Workaround

============================== CPU Info

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 45 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz CPU family: 6 Model: 85 Thread(s) per core: 1 Core(s) per socket: 1 Socket(s): 16 Stepping: 7 BogoMIPS: 5786.40 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 16 MiB (16 instances) L3 cache: 572 MiB (16 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Gather data sampling: Vulnerable: No microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT disabled Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS 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 SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

PR fix notes

PR #37984: [Doc] Add configurable /dev/shm shared memory mount to helm chart

Description (problem / solution / changelog)

Purpose

Fixes #37982

Fixes the configuration gap where examples/online_serving/chart-helm did not support configuring /dev/shm shared memory.

This change adds configurable shared memory support via shm in values.yaml, and conditionally renders:

  • a /dev/shm volumeMount
  • a memory-backed emptyDir volume with sizeLimit (sizeLimit is not specified if shm.size is unset or empty)

This aligns the Helm chart with official vLLM Kubernetes guidance that explicitly uses /dev/shm for tensor parallel inference:

Test Plan

1. Helm template validation

  • helm template test examples/online_serving/chart-helm
  • helm template test examples/online_serving/chart-helm --set shm.enabled=true --set shm.size="8Gi"
  • helm template test examples/online_serving/chart-helm --set shm.enabled=true --set shm.size=""

2. Kubernetes deployment validation (real cluster)

  • Deploy with --set shm.enabled=true --set shm.size="8Gi"
  • Verify /dev/shm mount and volume configuration:
    • kubectl get pod -n <namespace>
    • kubectl get pod <pod-name> -n <namespace> -oyaml | grep -E 'name: shm|mountPath: /dev/shm|medium: Memory|sizeLimit:'
    • kubectl exec <pod-name> -c vllm -n <namespace> -- df -h /dev/shm

Test Result

  • helm template validation passed
  • Real cluster deployment validation
$ kubectl get pod -n models
NAME                                                              READY   STATUS    RESTARTS   AGE
quanttrio-qwen3-vl-30b-a3b-instruct-awq-deployment-vllm-56z4bnx   1/1     Running   0          7m52s
$ kubectl get pod quanttrio-qwen3-vl-30b-a3b-instruct-awq-deployment-vllm-56z4bnx -n models -oyaml | grep -E 'name: shm|mountPath: /dev/shm|medium: Memory|sizeLimit:'
    - mountPath: /dev/shm
      medium: Memory
      sizeLimit: 8Gi
    - mountPath: /dev/shm
$ kubectl exec quanttrio-qwen3-vl-30b-a3b-instruct-awq-deployment-vllm-56z4bnx -c vllm -n models -- df -h /dev/shm
Filesystem      Size  Used Avail Use% Mounted on
tmpfs           8.0G  4.0K  8.0G   1% /dev/shm

<details> <summary> Essential Elements of an Effective PR Description Checklist </summary>
  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
  • (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.
</details>

Changed files

  • docs/deployment/frameworks/helm.md (modified, +1/-0)
  • examples/online_serving/chart-helm/templates/deployment.yaml (modified, +13/-1)
  • examples/online_serving/chart-helm/values.schema.json (modified, +12/-1)
  • examples/online_serving/chart-helm/values.yaml (modified, +9/-0)

Code Example

==============================
        System Info
==============================
OS                           : Ubuntu 24.04 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.39

==============================
       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               : 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-6.8.0-90-generic-x86_64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   :
GPU models and configuration : GPU 0: NVIDIA A100-PCIE-40GB
Nvidia driver version        : 570.195.03
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:                        45 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   1
Core(s) per socket:                   1
Socket(s):                            16
Stepping:                             7
BogoMIPS:                             5786.40
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user
L1d cache:                            512 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             16 MiB (16 instances)
L3 cache:                             572 MiB (16 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-15
Vulnerability Gather data sampling:   Vulnerable: No microcode
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT disabled
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
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 SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Mitigation; IBPB before exit to userspace

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[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==1.18.0
[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==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[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==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-15    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
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_<user>
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>
==============================
        System Info
==============================
OS                           : Ubuntu 24.04 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.39

==============================
       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               : 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-6.8.0-90-generic-x86_64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   :
GPU models and configuration : GPU 0: NVIDIA A100-PCIE-40GB
Nvidia driver version        : 570.195.03
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:                        45 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   1
Core(s) per socket:                   1
Socket(s):                            16
Stepping:                             7
BogoMIPS:                             5786.40
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user
L1d cache:                            512 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             16 MiB (16 instances)
L3 cache:                             572 MiB (16 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-15
Vulnerability Gather data sampling:   Vulnerable: No microcode
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT disabled
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
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 SW loop, KVM SW loop
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
Vulnerability Vmscape:                Mitigation; IBPB before exit to userspace

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[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==1.18.0
[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==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[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==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-15    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
==============================
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_<user>
</details>

🐛 Describe the bug

This issue concerns chart-helm.

This Helm chart does not provide a way to configure shared memory (/dev/shm) for the vLLM container.

This is a deployment gap because official vLLM docs explicitly show /dev/shm setup for Kubernetes and explain that it is needed for tensor parallel inference / IPC.

Evidence from official docs

1. Kubernetes deployment docs include this comment and manifest pattern:

vLLM needs to access the host's shared memory for tensor parallel inference.

and show:

  • volumes[].emptyDir.medium: Memory with sizeLimit
  • volumeMounts[].mountPath: /dev/shm

Ref:

2. Parallelism/scaling docs also state Kubernetes pods should mount /dev/shm for IPC:

Shared memory with /dev/shm: mount /dev/shm in the pod spec to provide shared memory for interprocess communication (IPC).

Ref:

3. Installation docs mention vLLM/PyTorch shared memory requirement

You can either use the ipc=host flag or --shm-size flag to allow the container to access the host's shared memory. vLLM uses PyTorch, which uses shared memory to share data between processes under the hood, particularly for tensor parallel inference.

Ref:

Current behavior

The Helm chart does not provide a way to configure /dev/shm, so users cannot set shared memory size via values.yaml.

Expected behavior

Helm values should support configuring shared memory, e.g.:

  • shmSize: 16Gi (or unset/empty to disable)

and render:

  • volumeMounts entry at /dev/shm
  • corresponding volumes entry with emptyDir.medium: Memory and sizeLimit

Why this should be treated as a bug

The chart behavior is inconsistent with official deployment guidance and can lead to unstable or suboptimal tensor parallel deployments when default shared memory is insufficient.

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

To address the issue, we need to modify the Helm chart to support configuring shared memory (/dev/shm) for the vLLM container. Here are the steps:

  • Update the values.yaml file to include a new parameter shmSize with a default value (e.g., 16Gi).
  • Modify the template/deployment.yaml file to include a volumeMounts entry at /dev/shm and a corresponding volumes entry with emptyDir.medium: Memory and sizeLimit set to the value of shmSize.

Example code changes:

# values.yaml
shmSize: 16Gi
# template/deployment.yaml
spec:
  containers:
  - name: vllm
    ...
    volumeMounts:
    - name: shm
      mountPath: /dev/shm
    ...
  volumes:
  - name: shm
    emptyDir:
      medium: Memory
      sizeLimit: {{ .Values.shmSize }}

Verification

To verify the fix, you can:

  • Update the values.yaml file with a custom shmSize value (e.g., 32Gi).
  • Run helm install or helm upgrade to deploy the updated chart.
  • Check the deployed pod's configuration to ensure that the shmSize value is applied correctly.

Example command:

helm install my-release chart-helm -f values.yaml --set shmSize=32Gi

Extra Tips

  • Make sure to update the Helm chart documentation to reflect the new shmSize parameter.
  • Consider adding a validation check to ensure that the shmSize value is a valid memory size (e.g., 16Gi, 32Mi, etc.).

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FAQ

Expected behavior

Helm values should support configuring shared memory, e.g.:

  • shmSize: 16Gi (or unset/empty to disable)

and render:

  • volumeMounts entry at /dev/shm
  • corresponding volumes entry with emptyDir.medium: Memory and sizeLimit

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