feat(gpu): serve 6 models on worker-1 via KServe — vLLM v0.11.0 (bitsandbytes) + Ollama + TEI, plus RuntimeClass/privileged-PSA prereqs and a local-NVMe StorageClass, working around Volta sm_70 limits

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.
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+# Action engine — Ornith-1.0-35B on Ollama.
+#
+# Why not vLLM like the other two: Ornith is
+# Qwen3_5MoeForConditionalGeneration (Qwen3.5 MoE, 256 experts / 8 active,
+# hybrid attention — 30 linear_attention + 10 full_attention layers). vLLM's
+# Qwen3.5 support landed 2026-07-29, AFTER vLLM dropped Volta (sm_70) at
+# v0.11.1. No vLLM build has both, so Ornith cannot run on vLLM on a V100.
+#
+# Ollama ships `ornith:35b` in its library and runs a llama-server runner
+# underneath, which keeps Volta support. q4 is ~21GB — fits one 32GB V100 with
+# room for KV.
+#
+# OLLAMA_KEEP_ALIVE=-1 is load-bearing: the harness calls this every loop
+# iteration, and Ollama's default is to evict an idle model after 5m, which
+# would add a ~21GB reload to a random future request.
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: ornith
+  labels:
+    app.kubernetes.io/name: llm-ornith
+    app.kubernetes.io/part-of: llm-serving
+spec:
+  predictor:
+    minReplicas: 1
+    # Recreate, not the default RollingUpdate: GPUs are allocated exactly 4/4,
+    # so a surge pod has no card to claim and sits Pending while the old pod is
+    # never torn down — a deadlock. Recreate tears down first, accepting a brief
+    # gap during updates.
+    deploymentStrategy:
+      type: Recreate
+    maxReplicas: 1
+    nodeSelector:
+      kubernetes.io/hostname: worker-1
+    runtimeClassName: nvidia
+    containers:
+      - name: kserve-container
+        image: ollama/ollama:0.32.9@sha256:1685741456770df6e3cceb2a945a5f75e020f658d1701509668d6f4688f1dd3f
+        # `ollama serve` does not pull models, and `ollama pull` needs a running
+        # server — so background the server, wait for it, pull, then hand the
+        # foreground back to serve.
+        command:
+          - /bin/sh
+          - -c
+          - |
+            set -e
+            ollama serve &
+            SERVE_PID=$!
+            until ollama list >/dev/null 2>&1; do sleep 2; done
+            ollama pull ornith:35b
+            ollama pull qwen2.5:3b-instruct
+            wait $SERVE_PID
+        env:
+          # Match the port the other two engines use.
+          - name: OLLAMA_HOST
+            value: "0.0.0.0:8080"
+          - name: OLLAMA_MODELS
+            value: /mnt/models/ollama
+          # Ollama defaults to a 4096 context, far too small for an agentic
+          # coding model. Ornith's hybrid attention means only 10 of its 40
+          # layers hold a conventional KV cache, so 32K is affordable in the
+          # ~11GiB left after its 21GB of weights.
+          - name: OLLAMA_CONTEXT_LENGTH
+            value: "32768"
+          # Never evict — this model is on the harness's hot path.
+          - name: OLLAMA_KEEP_ALIVE
+            value: "-1"
+          # Serial agent loop; no benefit from parallel slots.
+          - name: OLLAMA_NUM_PARALLEL
+            value: "1"
+          # 2, so ornith and the small utility model stay co-resident on GPU2
+          # instead of evicting one another on every alternating request.
+          - name: OLLAMA_MAX_LOADED_MODELS
+            value: "2"
+        ports:
+          - containerPort: 8080
+            protocol: TCP
+        resources:
+          requests:
+            cpu: "8"
+            memory: 8Gi
+            nvidia.com/gpu: "1"
+          limits:
+            cpu: "16"
+            memory: 16Gi
+            nvidia.com/gpu: "1"
+        volumeMounts:
+          - name: models
+            mountPath: /mnt/models
+        # Probes must confirm the MODEL is present, not just that the server
+        # answers. Ollama's `GET /` returns 200 ("Ollama is running") the moment
+        # `ollama serve` binds — which is before the ~21GB pull finishes. An
+        # httpGet probe would therefore mark this pod Ready with no model
+        # loaded, and KServe would route traffic to it.
+        startupProbe:
+          exec:
+            command: ["/bin/sh", "-c", "ollama list 2>/dev/null | grep -q ornith && ollama list 2>/dev/null | grep -q qwen2.5"]
+          periodSeconds: 15
+          failureThreshold: 120
+        readinessProbe:
+          exec:
+            command: ["/bin/sh", "-c", "ollama list 2>/dev/null | grep -q ornith && ollama list 2>/dev/null | grep -q qwen2.5"]
+          periodSeconds: 10
+    volumes:
+      - name: models
+        persistentVolumeClaim:
+          claimName: llm-models