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.

k8s/apps/llm-serving/namespace.yaml

@@ -0,0 +1,4 @@
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: llm-serving