[Bug]: PD-disaggregation cannot use PCIe P2P for KV transfer on Intel Arc Pro B60 (BMG)

[sharvil10]

Your current environment

The output of python collect_env.py from docker.io/vllm/vllm-openai-xpu:latest

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : version 4.4.2
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.13.0+xpu
Is debug build               : False
CUDA used to build PyTorch   : None
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : 20260000

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Jun 19 2026, 12:46:00) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-7.0.0-14-generic-x86_64-with-glibc2.39
    
==============================
      Intel XPU / GPU Info
==============================
Is XPU available             : True
XPU runtime version          : 20260000
Intel GPU models             : GPU 0: Intel(R) Arc(TM) Pro B60 Graphics

--Compile time--
oneAPI compiler version      : Could not collect
SYCL compiler build          : Could not collect
oneCCL version               : 2022.0.0

--Runtime--
Intel Graphics Compiler (IGC): 2.34.4
Intel GMM (libigdgmm)        : 22.10.0
Level Zero loader version    : 1.28.2
Level Zero driver version    : 26.18.38308.1-0
vLLM XPU kernels version     : 0.1.12

==============================
          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):                                  512
On-line CPU(s) list:                     0-511
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) 6980P
CPU family:                              6
Model:                                   173
Thread(s) per core:                      2
Core(s) per socket:                      128
Socket(s):                               2
Stepping:                                1
CPU(s) scaling MHz:                      21%
CPU max MHz:                             3900.0000
CPU min MHz:                             800.0000
BogoMIPS:                                4000.00
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 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:                               12 MiB (256 instances)
L1i cache:                               16 MiB (256 instances)
L2 cache:                                512 MiB (256 instances)
L3 cache:                                1008 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-127,256-383
NUMA node1 CPU(s):                       128-255,384-511
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: 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 Old microcode:             Vulnerable
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; PBRSB-eIBRS Not affected; BHI BHI_DIS_S
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] pyzmq==27.1.0
[pip3] sentence-transformers==5.3.0
[pip3] torch==2.13.0+xpu
[pip3] torchaudio==2.11.0+xpu
[pip3] torchcodec==0.15.0+cpu
[pip3] torchvision==0.28.0+xpu
[pip3] transformers==5.14.1
[pip3] triton-xpu==3.7.2
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.27.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Enabled
GPU Topology:
  Could not collect

==============================
     Environment Variables
==============================
VLLM_WORKER_MULTIPROC_METHOD=spawn
VLLM_TARGET_DEVICE=xpu
LD_LIBRARY_PATH=/tmp/ucx_install/lib:/opt/venv/lib:/usr/local/lib
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

+ echo '==== EXIT: 0 ===='
+ sleep 30
==== EXIT: 0 ====

Image / stack summary:

• Image: docker.io/vllm/vllm-openai-xpu:latest — digest sha256:3309c2e1f7b1048fea9f392423afd91080c9f9e63796692872cff477e2562220 — same digest as vllm/vllm-openai-xpu:v0.27.1.
• vLLM: v0.27.1.
• GPU: 2× Intel Arc Pro B60 (BMG G21, PCI 8086:e211), same physical host, one B60 per pod.
• Host: Xeon 6980P (Granite Rapids-AP), kernel 7.0.0-14-generic, xe DRM driver, Ubuntu 26.04 host.
• NEO 26.18.38308.1 / L0 loader 1.28.2 as shipped in the image above.

🐛 Describe the bug

On the latest published vLLM XPU image, cross-pod PD-disaggregation on Intel Arc Pro B60 (BMG) has no path for VRAM-to-VRAM KV transfer over PCIe P2P. The only working configuration is kv_buffer_device: cpu with UCX_TLS=tcp, which forces every KV block through host DRAM.

Scope note. This is a same-node PCIe P2P case; we don't have RDMA NICs on these boxes. Cross-node with GPUDirect RDMA is out of scope. Ideally cross-pod KV on the same physical host would move VRAM-to-VRAM over PCIe without touching host DRAM. The PCIe P2P fabric itself is fine at the kernel/driver level: an in-process cross-GPU probe (torch.xpu.tensor.to(other_xpu)) sustains ~22 GB/s per pair with essentially zero DDR traffic (perf stat -e uncore_imc/cas_count_*/) — so the gap is entirely in the userspace stack.

What happens on vllm/vllm-openai-xpu:latest:

• Image ships UCX 1.21.0 built --with-ze=yes, which provides ze and ze_copy transports but not ze_ipc. The ze_ipc transport isn't in any released UCX; it's proposed in openucx/ucx#11218, still unmerged.
• With kv_buffer_device: xpu + UCX_TLS=ze_ipc,…, UCX prints:

  UCX  WARN  transport 'ze_ipc' is not available, please use one or more of:
             cma, mm, posix, self, shm, sm, sysv, tcp, ze, ze_copy
  

  and the NIXL agent fails to init.
• The only serving configuration we can bring up on BMG today is kv_buffer_device: cpu + UCX_TLS=tcp. Measured on Qwen3-8B at ISL=4096: ~576 MB KV moved per request, ~633 MB/s, ~900 ms transfer wall — all of it via host DDR.

What we tried: built a custom vLLM XPU image from main with UCX rebuilt from openucx/ucx#11218 and NEO bumped to 26.31.39395.13. In that image UCX 1.23 picks ze_ipc/ze_ipc for the KV transfer as intended, and zeMemOpenIpcHandle on the peer's exported buffer returns a valid mapped pointer. The transfer itself then fails at the Level Zero driver:

ze_ipc_ep.c:277  UCX ERROR  zeCommandListAppendMemoryCopy failed with error 0x70000003
ze_ipc_ep.c:323  UCX ERROR  ze_ipc_ep: GET_ZCOPY failed with status Input/output error
postXferReq: backend 'UCX' failed with status NIXL_ERR_BACKEND

0x70000003 = ZE_RESULT_ERROR_INVALID_ARGUMENT. NEO on BMG accepts the dma-buf import but refuses to use the resulting pointer as the source of a copy-engine command. This is the same error family being discussed in intel/compute-runtime#935 and remains unresolved as of NEO 26.31 on BMG + kernel 7.0.0-14.

Practical situation for vLLM users on BMG today: no P2P KV path is available end-to-end. The public image lacks ze_ipc; a from-scratch build with the upstream PR unblocks the vLLM/UCX layer but hits an Intel driver bug the next step down.

Guidance we'd appreciate:

• Is there a supported path for VRAM-to-VRAM PD KV transfer on Intel BMG on v0.27.1 that we're missing?
• Is there planned work on the vLLM XPU Dockerfile to bump UCX to include ze_ipc once openucx/ucx#11218 lands, and NEO to a driver version where cross-process IPC copy is fixed on BMG?

Deployment configuration used to reproduce:

prefill-deployment.yaml

# Standalone prefill Deployment used to reproduce the report.
#
# Adapt the following to your own environment:
#   * `image`  — swap for the vLLM XPU image you're testing
#   * `MODEL`  — Hugging Face model id (auto-downloaded on first run, or
#                pre-cached and mounted via a volume you provide)
#   * `resources.claims` + the ResourceClaimTemplate reference — this uses
#                Kubernetes DRA for the Intel GPU. If your cluster doesn't
#                have Intel DRA installed, replace with `resources.limits`
#                (e.g. `gpu.intel.com/i915: 1`) or whatever device-plugin
#                shape your setup uses.
#   * `HF_TOKEN` — provide via a Secret you own, or drop the env if the
#                model is already on-disk.
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-pd-prefill
  labels:
    app.kubernetes.io/name: vllm-pd
    app.kubernetes.io/component: prefill
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: vllm-pd
      app.kubernetes.io/component: prefill
  template:
    metadata:
      labels:
        app.kubernetes.io/name: vllm-pd
        app.kubernetes.io/component: prefill
    spec:
      # /dev/dri/render* group; Intel GPU renderer nodes typically use gid 107.
      securityContext:
        fsGroup: 107
        supplementalGroups: [107]
      resourceClaims:
        - name: xpu
          resourceClaimTemplateName: xpu-prefill-claim
      containers:
        - name: vllm
          image: docker.io/vllm/vllm-openai-xpu:latest
          imagePullPolicy: IfNotPresent
          command: ["vllm", "serve"]
          args:
            - "Qwen/Qwen3-8B"
            - "--served-model-name=Qwen/Qwen3-8B"
            - "--port=8000"
            - "--tensor-parallel-size=1"
            - "--block-size=64"
            - "--kv-transfer-config"
            - '{"kv_connector":"NixlConnector","kv_role":"kv_producer","kv_buffer_device":"xpu"}'
            - "--disable-uvicorn-access-log"
            - "--disable-sliding-window"
            - "--dtype=bfloat16"
            - "--enforce-eager"
            - "--gpu-memory-utilization=0.93"
            - "--max-num-batched-tokens=8192"
            - "--no-enable-prefix-caching"
            - "--max-model-len=8192"
          ports:
            - name: http
              containerPort: 8000
            - name: nixl
              containerPort: 5600
          env:
            # Set to the transports you want UCX to consider. On the baseline
            # image (UCX 1.21, no ze_ipc), leave as `tcp` and use
            # `kv_buffer_device: cpu` above.
            - name: UCX_TLS
              value: "ze_ipc,ze_copy,sm,cma,tcp,self"
            - name: UCX_LOG_LEVEL
              value: "info"
            - name: VLLM_NIXL_SIDE_CHANNEL_HOST
              valueFrom:
                fieldRef:
                  fieldPath: status.podIP
            - name: VLLM_NIXL_SIDE_CHANNEL_PORT
              value: "5600"
            - name: VLLM_WORKER_MULTIPROC_METHOD
              value: "spawn"
            # Optional — only if you gate model downloads through your own HF token.
            # - name: HF_TOKEN
            #   valueFrom:
            #     secretKeyRef:
            #       name: hf-token
            #       key: HF_TOKEN
          resources:
            requests:
              cpu: "8"
              memory: 16Gi
            limits:
              cpu: "16"
              memory: 32Gi
            claims:
              - name: xpu
          volumeMounts:
            - name: shm
              mountPath: /dev/shm
            - name: cache
              mountPath: /.cache
          startupProbe:
            httpGet: {path: /v1/models, port: 8000}
            periodSeconds: 30
            timeoutSeconds: 10
            failureThreshold: 60
          readinessProbe:
            httpGet: {path: /v1/models, port: 8000}
            periodSeconds: 10
            timeoutSeconds: 5
            failureThreshold: 6
      volumes:
        - name: shm
          emptyDir:
            medium: Memory
            sizeLimit: 20Gi
        - name: cache
          emptyDir: {}

decode-deployment.yaml

# Standalone decode Deployment. See prefill-deployment.yaml for the
# environment-specific fields you'll want to adapt (image, model id,
# DRA claim shape, optional HF_TOKEN).
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-pd-decode
  labels:
    app.kubernetes.io/name: vllm-pd
    app.kubernetes.io/component: decode
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: vllm-pd
      app.kubernetes.io/component: decode
  template:
    metadata:
      labels:
        app.kubernetes.io/name: vllm-pd
        app.kubernetes.io/component: decode
    spec:
      securityContext:
        fsGroup: 107
        supplementalGroups: [107]
      resourceClaims:
        - name: xpu
          resourceClaimTemplateName: xpu-decode-claim
      containers:
        - name: vllm
          image: docker.io/vllm/vllm-openai-xpu:latest
          imagePullPolicy: IfNotPresent
          command: ["vllm", "serve"]
          args:
            - "Qwen/Qwen3-8B"
            - "--served-model-name=Qwen/Qwen3-8B"
            - "--port=8200"
            - "--tensor-parallel-size=1"
            - "--block-size=64"
            - "--kv-transfer-config"
            - '{"kv_connector":"NixlConnector","kv_role":"kv_consumer","kv_buffer_device":"xpu"}'
            - "--disable-uvicorn-access-log"
            - "--disable-sliding-window"
            - "--dtype=bfloat16"
            - "--enforce-eager"
            - "--gpu-memory-utilization=0.93"
            - "--max-num-batched-tokens=8192"
            - "--no-enable-prefix-caching"
            - "--max-model-len=8192"
          ports:
            - name: http
              containerPort: 8200
            - name: nixl
              containerPort: 5600
          env:
            - name: UCX_TLS
              value: "ze_ipc,ze_copy,sm,cma,tcp,self"
            - name: UCX_LOG_LEVEL
              value: "info"
            - name: VLLM_NIXL_SIDE_CHANNEL_HOST
              valueFrom:
                fieldRef:
                  fieldPath: status.podIP
            - name: VLLM_NIXL_SIDE_CHANNEL_PORT
              value: "5600"
            - name: VLLM_WORKER_MULTIPROC_METHOD
              value: "spawn"
            # - name: HF_TOKEN
            #   valueFrom:
            #     secretKeyRef:
            #       name: hf-token
            #       key: HF_TOKEN
          resources:
            requests:
              cpu: "8"
              memory: 16Gi
            limits:
              cpu: "16"
              memory: 32Gi
            claims:
              - name: xpu
          volumeMounts:
            - name: shm
              mountPath: /dev/shm
            - name: cache
              mountPath: /.cache
          startupProbe:
            httpGet: {path: /v1/models, port: 8200}
            periodSeconds: 30
            timeoutSeconds: 10
            failureThreshold: 60
          readinessProbe:
            httpGet: {path: /v1/models, port: 8200}
            periodSeconds: 10
            timeoutSeconds: 5
            failureThreshold: 6
      volumes:
        - name: shm
          emptyDir:
            medium: Memory
            sizeLimit: 20Gi
        - name: cache
          emptyDir: {}

resource-claim-templates.yaml (Kubernetes DRA)

# Kubernetes DRA ResourceClaimTemplates for the two Intel B60 (BMG G21) GPUs.
# These require the Intel resource driver
# (github.com/intel/intel-resource-drivers-for-kubernetes) to be installed
# and the `gpu.intel.com` DeviceClass to exist on the cluster.
#
# If your cluster surfaces Intel GPUs via a different mechanism (e.g. the
# older intel/gpu-device-plugin), remove these and instead set the pod's
# containers[].resources.limits (e.g. `gpu.intel.com/i915: 1`).
apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: xpu-prefill-claim
spec:
  spec:
    devices:
      requests:
        - name: intel
          exactly:
            deviceClassName: gpu.intel.com
            count: 1
            selectors:
              - cel:
                  # pciId 0xe211 = Intel Arc Pro B60 (Battlemage G21).
                  # Change or drop this selector for other Intel GPU SKUs.
                  expression: device.attributes["gpu.intel.com"].pciId == "0xe211"
---
apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: xpu-decode-claim
spec:
  spec:
    devices:
      requests:
        - name: intel
          exactly:
            deviceClassName: gpu.intel.com
            count: 1
            selectors:
              - cel:
                  expression: device.attributes["gpu.intel.com"].pciId == "0xe211"

collect_env.py output from the custom (PR-based) image, for comparison

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : version 4.4.2
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.13.0+xpu
Is debug build               : False
CUDA used to build PyTorch   : None
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : 20260000

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Jun 19 2026, 12:46:00) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-7.0.0-14-generic-x86_64-with-glibc2.39
    
==============================
      Intel XPU / GPU Info
==============================
Is XPU available             : True
XPU runtime version          : 20260000
Intel GPU models             : GPU 0: Intel(R) Arc(TM) Pro B60 Graphics

--Compile time--
oneAPI compiler version      : Could not collect
SYCL compiler build          : Could not collect
oneCCL version               : 2022.0.0

--Runtime--
Intel Graphics Compiler (IGC): 2.40.13
Intel GMM (libigdgmm)        : 22.10.0
Level Zero loader version    : 1.32.0
Level Zero driver version    : 26.31.39395.13-0
vLLM XPU kernels version     : 0.1.13.2

==============================
          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):                                  512
On-line CPU(s) list:                     0-511
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) 6980P
CPU family:                              6
Model:                                   173
Thread(s) per core:                      2
Core(s) per socket:                      128
Socket(s):                               2
Stepping:                                1
CPU(s) scaling MHz:                      21%
CPU max MHz:                             3900.0000
CPU min MHz:                             800.0000
BogoMIPS:                                4000.00
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 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:                               12 MiB (256 instances)
L1i cache:                               16 MiB (256 instances)
L2 cache:                                512 MiB (256 instances)
L3 cache:                                1008 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-127,256-383
NUMA node1 CPU(s):                       128-255,384-511
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: 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 Old microcode:             Vulnerable
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; PBRSB-eIBRS Not affected; BHI BHI_DIS_S
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.3.5
[pip3] pyzmq==27.1.0
[pip3] sentence-transformers==5.7.0
[pip3] torch==2.13.0+xpu
[pip3] torchaudio==2.11.0+xpu
[pip3] torchcodec==0.16.0+cpu
[pip3] torchvision==0.28.0+xpu
[pip3] transformers==5.15.0
[pip3] triton==3.7.2+xpu
[pip3] triton-xpu==3.7.2
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev1+g6a9c69fa8.d20260824 (git sha: 6a9c69fa8, date: 20260824)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Enabled
GPU Topology:
  Could not collect

==============================
     Environment Variables
==============================
NEOReadDebugKeys=1
TORCH_LLM_ALLREDUCE=1
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
VLLM_NIXL_SIDE_CHANNEL_PORT=5600
VLLM_NIXL_SIDE_CHANNEL_HOST=10.0.0.<X>
VLLM_WORKER_MULTIPROC_METHOD=spawn
VLLM_TARGET_DEVICE=xpu
LD_LIBRARY_PATH=/tmp/ucx_install/lib:/opt/venv/lib:/usr/local/lib
VLLM_LOGGING_LEVEL=DEBUG
ZE_ENABLE_LOG=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

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