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Layout on 4x Tesla V100 32GB (PCIe, no NVLink), all TP=1: GPU0+1 vLLM DeepSeek-R1-Distill-Qwen-32B bnb-nf4 (2 replicas) GPU2 Ollama ornith:35b + qwen2.5:3b-instruct (co-resident) GPU3 vLLM Qwen2.5-Math-PRM-7B (reward model) CPU TEI nomic-embed-text-v2-moe, bge-reranker-base Volta constraints, each verified against live output rather than config: - vLLM pinned v0.11.0: sm_70 dropped from CUDA_SUPPORTED_ARCHS at v0.11.1. - AWQ hard-rejected (needs sm_75). GPTQ passes vLLM's min_capability=60 gate but is NUMERICALLY WRONG on sm_70 — emits garbage logits. Proven by an fp16 control run producing correct text on an identical backend. bitsandbytes nf4 verified correct by output. - flashinfer's check_cuda_arch() crashes on any sm_7x (calls .isdigit() on an int) -> VLLM_USE_FLASHINFER_SAMPLER=0. - xformers has no sm_70 kernel for V1's paged-attention bias, and V0 was removed in v0.11.0 -> TRITON_ATTN. - Ornith is Qwen3.5-MoE hybrid-attention; vLLM added that arch after dropping Volta, so no build has both -> Ollama, which also multiplexes a second model on the same card for free. Cluster prereqs that were absent: - RuntimeClass nvidia: the Talos toolkit extension registers the containerd handler but not the k8s object; without it every pod is rejected at admission. - gpu-system pinned to privileged PSA: a device plugin cannot satisfy the cluster-default baseline, it must mount hostPath. - device-plugin affinity=null: the chart requires NFD labels that do not exist here, so it matched zero nodes and reported desiredNumberScheduled=0 silently. - Recreate strategy on GPU services: with GPUs allocated exactly 4/4, a RollingUpdate surge pod has no card and deadlocks the rollout. - longhorn-llm-local SC (1 replica, strict-local, disk tag llm): the default 3-replica class could not place the volume at all (every control-plane disk was at its over-provisioning ceiling), and this keeps ~60GB of weights on worker-1's own NVMe instead of reading them over the network. deploy-gpu-serving.sh sequences ArgoCD syncs (or helm/kubectl in --manual mode) and never applies a manifest absent from git; doctor/unstick/teardown stages exist so this is diagnosable without ad-hoc kubectl archaeology.
102 lines
3.6 KiB
YAML
102 lines
3.6 KiB
YAML
# Verifier — Qwen2.5-Math-PRM-7B, step-level process reward model, vLLM.
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#
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# Model choice was constrained by vLLM v0.11.0's registry: its arch
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# (Qwen2ForProcessRewardModel) is natively registered, whereas the smaller
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# community PRMs are Qwen2ForTokenClassification / Qwen2ForPrmModel, neither of
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# which v0.11.0 can load (ForTokenClassification is absent from
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# _SUFFIX_TO_DEFAULTS, so it won't even auto-convert).
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#
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# --runner pooling, NOT --task reward: --task is [DEPRECATED] in v0.11.0.
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# Scoring goes to /pooling, not /v1/completions — this is a reward model, it
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# returns scores, not tokens.
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#
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# Gets a whole dedicated GPU despite only needing ~15Gi: it sits on the
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# harness's critical path (every reasoning->action->verify iteration waits on
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# it), so isolation from the generation engines' decode loops matters more than
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# the idle VRAM.
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apiVersion: serving.kserve.io/v1beta1
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kind: InferenceService
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metadata:
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name: verifier
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labels:
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app.kubernetes.io/name: llm-verifier
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app.kubernetes.io/part-of: llm-serving
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spec:
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predictor:
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minReplicas: 1
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# Recreate, not the default RollingUpdate: GPUs are allocated exactly 4/4,
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# so a surge pod has no card to claim and sits Pending while the old pod is
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# never torn down — a deadlock. Recreate tears down first, accepting a brief
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# gap during updates.
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deploymentStrategy:
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type: Recreate
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maxReplicas: 1
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nodeSelector:
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kubernetes.io/hostname: worker-1
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runtimeClassName: nvidia
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containers:
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- name: kserve-container
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image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
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args:
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- --model=Qwen/Qwen2.5-Math-PRM-7B
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- --served-model-name=verifier
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# Pooling runner => reward scoring. Weights are bf16; Volta needs fp16.
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- --runner=pooling
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- --dtype=float16
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- --tensor-parallel-size=1
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- --max-model-len=4096
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- --max-num-seqs=8
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- --host=0.0.0.0
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- --port=8080
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env:
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# flashinfer's check_cuda_arch() has an upstream bug that crashes on
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# ANY sm_7x GPU: `elif major == 7 and minor.isdigit()` calls .isdigit()
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# on an int, so instead of reporting "unsupported" it raises
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# AttributeError: 'int' object has no attribute 'isdigit'
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# and engine init dies. Default is None (auto-detect), which walks
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# straight into that path. 0 disables the flashinfer sampler outright.
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# Only affects the generate runner — the verifier (pooling) never hits
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# the sampler, which is why it started fine and this did not.
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- name: VLLM_USE_FLASHINFER_SAMPLER
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value: "0"
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- name: VLLM_ATTENTION_BACKEND
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value: XFORMERS
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- name: HF_HOME
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value: /mnt/models
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ports:
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- containerPort: 8080
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protocol: TCP
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resources:
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requests:
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cpu: "4"
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memory: 8Gi
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nvidia.com/gpu: "1"
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limits:
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cpu: "16"
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memory: 16Gi
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nvidia.com/gpu: "1"
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volumeMounts:
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- name: models
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mountPath: /mnt/models
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- name: shm
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mountPath: /dev/shm
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startupProbe:
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 15
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failureThreshold: 60
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readinessProbe:
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 10
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volumes:
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- name: models
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persistentVolumeClaim:
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claimName: llm-models
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- name: shm
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emptyDir:
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medium: Memory
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sizeLimit: 1Gi
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