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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.
75 lines
2.5 KiB
YAML
75 lines
2.5 KiB
YAML
# Embeddings — Nomic Embed Text v2 (MoE), on CPU via HuggingFace TEI.
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#
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# CPU, not GPU, deliberately. All 4 V100s are claimed by the generation models,
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# and the device plugin hands out WHOLE GPUs — a 5th GPU-requesting pod is
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# unschedulable no matter how much VRAM is free. Sharing would need global
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# time-slicing, which on a single node cannot be scoped to one card and would let
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# the scheduler co-locate two ~20GB models and OOM both.
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#
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# worker-1 has 96 cores with ~250m requested, and this is a 475M-param encoder
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# (305M active). Retrieval runs once per agent-loop iteration, not per token, so
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# CPU latency here is immaterial. This is also what Plan 1 originally specified.
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#
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# TEI (not vLLM) because it is purpose-built for encoders and explicitly lists
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# nomic-embed-text-v2-moe as supported.
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#
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# NOTE: Nomic v2 requires task prefixes on the CLIENT side —
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# documents: "search_document: <text>"
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# queries: "search_query: <text>"
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# Embedding without the prefix silently degrades retrieval quality.
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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: embeddings
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labels:
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app.kubernetes.io/name: llm-embeddings
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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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maxReplicas: 1
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# Pinned to worker-1 only so it can share the RWO models PVC with the GPU
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# pods (RWO = single node, any number of pods on it).
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nodeSelector:
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kubernetes.io/hostname: worker-1
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containers:
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- name: kserve-container
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image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.8.2@sha256:4d632b76bd14cb57044a1ffb0ad48ab0ba4939e705a9a615ccc740658575c26e
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args:
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- --model-id=nomic-ai/nomic-embed-text-v2-moe
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- --port=8080
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- --hostname=0.0.0.0
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# Truncate rather than 413 on over-long input.
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- --auto-truncate
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env:
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- name: HUGGINGFACE_HUB_CACHE
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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: "8"
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memory: 4Gi
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limits:
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cpu: "16"
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memory: 8Gi
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volumeMounts:
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- name: models
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mountPath: /mnt/models
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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: 10
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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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