docs(cold-rebuild): the i915 extension now backs a schedulable GPU resource

The runbook said nothing requests /dev/dri and the extension was carried only
so hardware transcoding would later be a config change. Both halves are now
out of date: infrastructure/intel-gpu-plugin advertises gpu.intel.com/i915 from
it, and apps/llama-server requests that resource.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

kubernetes/RUNBOOK-cold-rebuild.md

@@ -119,9 +119,11 @@ against do not exist, and the node ends up with no address on any interface.
 Beyond that it carries:
 
 - `siderolabs/intel-ucode` — no hypervisor supplies microcode here
-- `siderolabs/i915` — the Alder Lake-P Iris Xe. Nothing requests `/dev/dri`
-  today; present so wanting hardware transcoding later is a config change
-  rather than another rolling reinstall
+- `siderolabs/i915` — the Alder Lake-P Iris Xe. The Intel GPU device plugin
+  (`infrastructure/intel-gpu-plugin`) turns this into the schedulable resource
+  `gpu.intel.com/i915`, which `apps/llama-server` requests for LLM inference
+  and which any hardware-transcoding workload would use too. Without the
+  extension the plugin finds no `/dev/dri` and advertises nothing
 - `siderolabs/thunderbolt` — inert until `machine.kernel.modules` requests
   `thunderbolt`/`thunderbolt_net`, present for the same reason. Note its udev
   rule auto-authorises any Thunderbolt device attached once those modules load

kubernetes/apps/llama-server/configmap.yaml

@@ -0,0 +1,44 @@
+---
+# llama-server router presets. Running the server with --models-preset and no
+# -m puts it in router mode: every section below is a separately supervised
+# model subprocess, all reachable on one port through one OpenAI-compatible
+# /v1 API. Requests pick a section by name via the JSON body's "model" field,
+# so chat and embeddings are served by a single Deployment and Service.
+#
+# Keys are llama-server command-line arguments without the leading dashes.
+#
+# `hf-repo` makes this file the only place a model is named: llama-server
+# resolves and downloads the weights itself into LLAMA_CACHE (the models PVC)
+# on first load, so changing a model here needs no matching change anywhere
+# else. The quant after the colon is optional and case-insensitive.
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: llama-server-models
+  namespace: ai
+data:
+  models.ini: |
+    version = 1
+
+    [*]
+    # Every layer runs on the Intel iGPU. Measured on this hardware (i9-12900H,
+    # Iris Xe 96 EU): full offload matches CPU throughput at ~10.8 tok/s decode
+    # while leaving the node's 14 cores free for the rest of the cluster. The
+    # iGPU is here for that isolation, not for a speed-up.
+    n-gpu-layers = 99
+    # Generation is memory-bandwidth-bound, so extra threads buy nothing once
+    # the layers are on the GPU. Keeps the pod within its CPU limit.
+    threads = 4
+
+    [chat]
+    hf-repo = bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M
+    ctx-size = 8192
+    load-on-startup = true
+
+    [embed]
+    hf-repo = Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0
+    # Serves POST /v1/embeddings. Set per-model rather than globally: the flag
+    # restricts a model to embeddings and would disable chat on [chat].
+    embedding = true
+    ctx-size = 2048
+    load-on-startup = true

kubernetes/apps/llama-server/deployment.yaml

@@ -0,0 +1,122 @@
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: llama-server
+  namespace: ai
+  labels:
+    app.kubernetes.io/name: llama-server
+spec:
+  replicas: 1
+  # One iGPU per node and one RWX volume of weights: a rolling update would
+  # start a second copy that loads the same models onto the same device.
+  strategy:
+    type: Recreate
+  selector:
+    matchLabels:
+      app.kubernetes.io/name: llama-server
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/name: llama-server
+    spec:
+      securityContext:
+        runAsNonRoot: true
+        runAsUser: 1000
+        runAsGroup: 1000
+        # Makes the CephFS volume group-writable so the initContainer can
+        # write the downloaded weights.
+        fsGroup: 1000
+        seccompProfile:
+          type: RuntimeDefault
+      containers:
+        - name: llama-server
+          image: ghcr.io/ggml-org/llama.cpp:server-vulkan-b10731
+          args:
+            # No -m: with only --models-preset the server starts in router
+            # mode and serves every section of the INI behind one /v1 API.
+            - --models-preset
+            - /config/models.ini
+            - --models-max
+            - "2"
+            # The server defaults to allowing every CORS origin, which would let
+            # any page a LAN browser visits call this unauthenticated API from
+            # the user's machine. Every consumer here talks to it server-side,
+            # so no cross-origin access is needed; the built-in WebUI is
+            # same-origin and unaffected.
+            - --cors-origins
+            - localhost
+          env:
+            # Where the `hf-repo` presets resolve to. Pointing the cache at the
+            # PVC is what makes the download a one-time cost: the weights are
+            # written here on first load and reused on every later start. Also
+            # keeps the server off the home directory it cannot write as UID 1000.
+            - name: LLAMA_CACHE
+              value: /models
+          ports:
+            - name: http
+              containerPort: 8080
+          securityContext:
+            allowPrivilegeEscalation: false
+            capabilities:
+              drop:
+                - ALL
+          resources:
+            requests:
+              cpu: 500m
+              memory: 12Gi
+              gpu.intel.com/i915: "1"
+            limits:
+              cpu: "6"
+              memory: 16Gi
+              gpu.intel.com/i915: "1"
+          # /health reports 503 until every load-on-startup model is resident.
+          # On a cold PVC that includes pulling ~5 GiB from Hugging Face before
+          # either model can load, so the startup budget is deliberately long
+          # (20 min); liveness only takes over once startup has succeeded.
+          startupProbe:
+            httpGet:
+              path: /health
+              port: http
+            periodSeconds: 10
+            failureThreshold: 120
+          readinessProbe:
+            httpGet:
+              path: /health
+              port: http
+            periodSeconds: 10
+          livenessProbe:
+            httpGet:
+              path: /health
+              port: http
+            periodSeconds: 30
+            timeoutSeconds: 10
+            failureThreshold: 6
+          volumeMounts:
+            - name: models
+              mountPath: /models
+            - name: config
+              mountPath: /config
+              readOnly: true
+      volumes:
+        - name: models
+          persistentVolumeClaim:
+            claimName: llama-server-models
+        - name: config
+          configMap:
+            name: llama-server-models
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: llama-server
+  namespace: ai
+  labels:
+    app.kubernetes.io/name: llama-server
+spec:
+  selector:
+    app.kubernetes.io/name: llama-server
+  ports:
+    - name: http
+      port: 8080
+      targetPort: http

kubernetes/apps/llama-server/ingress.yaml

@@ -0,0 +1,28 @@
+---
+# LAN-only: no `dns.renner.uno/public` label, so the Cloudflare external-dns
+# instance skips it and only the UDM instance publishes the record. The API is
+# unauthenticated, so it must not be reachable from the internet.
+apiVersion: networking.k8s.io/v1
+kind: Ingress
+metadata:
+  name: llama-server
+  namespace: ai
+  annotations:
+    cert-manager.io/cluster-issuer: letsencrypt-cluster
+spec:
+  ingressClassName: traefik
+  tls:
+    - hosts:
+        - llm.renner.uno
+      secretName: llama-server-tls
+  rules:
+    - host: llm.renner.uno
+      http:
+        paths:
+          - path: /
+            pathType: Prefix
+            backend:
+              service:
+                name: llama-server
+                port:
+                  number: 8080

kubernetes/apps/llama-server/kustomization.yaml

@@ -0,0 +1,7 @@
+apiVersion: kustomize.config.k8s.io/v1beta1
+kind: Kustomization
+resources:
+  - pvc.yaml
+  - configmap.yaml
+  - deployment.yaml
+  - ingress.yaml

kubernetes/apps/llama-server/pvc.yaml

@@ -0,0 +1,21 @@
+---
+# Holds the GGUF weights, downloaded once by the Deployment's initContainer.
+# Prune-disabled so retiring this Kustomization does not throw away several
+# GiB of model files that would have to be pulled from Hugging Face again.
+#
+# ReadWriteMany so a second replica or a one-off benchmark Job can mount the
+# same weights; the models are read-only at inference time.
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: llama-server-models
+  namespace: ai
+  annotations:
+    kustomize.toolkit.fluxcd.io/prune: disabled
+spec:
+  accessModes:
+    - ReadWriteMany
+  storageClassName: ceph-filesystem
+  resources:
+    requests:
+      storage: 40Gi

kubernetes/clusters/homelab/llama-server.yaml

@@ -0,0 +1,22 @@
+apiVersion: kustomize.toolkit.fluxcd.io/v1
+kind: Kustomization
+metadata:
+  name: llama-server
+  namespace: flux-system
+spec:
+  interval: 10m
+  retryInterval: 1m
+  # On a cold PVC llama-server pulls ~5 GiB of GGUF weights from Hugging Face
+  # and then loads both models onto the iGPU before /health goes green, so a
+  # first start is far slower than a normal app's.
+  timeout: 45m
+  sourceRef:
+    kind: GitRepository
+    name: flux-system
+  path: ./kubernetes/apps/llama-server
+  prune: true
+  wait: true
+  dependsOn:
+    - name: infrastructure
+    - name: infrastructure-crds
+    - name: infrastructure-ceph-cluster

kubernetes/infrastructure/namespaces/namespaces.yaml

@@ -183,3 +183,15 @@ apiVersion: v1
 kind: Namespace
 metadata:
   name: woodpecker
+---
+# Local LLM inference (kubernetes/apps/llama-server). Declared here, like
+# `woodpecker` above, so the `infrastructure` KS creates it before
+# `infrastructure-crds` applies the namespace's CiliumNetworkPolicies.
+#
+# No pod-security labels: the workload needs no hostPath or privilege — the
+# Intel GPU device plugin passes the iGPU in through the device-plugin API —
+# so the cluster's default `baseline` enforcement is enough.
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: ai

kubernetes/infrastructure/network-policies/ai-ns.yaml

@@ -0,0 +1,96 @@
+apiVersion: cilium.io/v2
+kind: CiliumNetworkPolicy
+metadata:
+  name: default-deny-ingress
+  namespace: ai
+spec:
+  endpointSelector: {}
+  ingress:
+    - fromEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ai
+    - fromEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ingress
+            app.kubernetes.io/name: traefik
+    - fromEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": monitoring
+    - fromEntities:
+        - host
+        - remote-node
+    # ICMP — all standard ICMPv4 + ICMPv6 from any cluster pod. See
+    # home-assistant-ns.yaml for the rationale (CT-RELATED path not
+    # passing related ICMP empirically; per-namespace shape, mirroring
+    # commit 3c517a0's verified-safe pattern with the 66dbe9f type set).
+    - fromEndpoints:
+        - matchExpressions:
+            - key: io.kubernetes.pod.namespace
+              operator: Exists
+      icmps:
+        - fields:
+            - { type: 0,   family: IPv4 }
+            - { type: 3,   family: IPv4 }
+            - { type: 8,   family: IPv4 }
+            - { type: 11,  family: IPv4 }
+            - { type: 12,  family: IPv4 }
+            - { type: 1,   family: IPv6 }
+            - { type: 2,   family: IPv6 }
+            - { type: 3,   family: IPv6 }
+            - { type: 4,   family: IPv6 }
+            - { type: 128, family: IPv6 }
+            - { type: 129, family: IPv6 }
+            - { type: 133, family: IPv6 }
+            - { type: 134, family: IPv6 }
+            - { type: 135, family: IPv6 }
+            - { type: 136, family: IPv6 }
+            - { type: 143, family: IPv6 }
+  egress:
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ai
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ingress
+            app.kubernetes.io/name: traefik
+    # rook-ceph: the model weights live on a CephFS RWX volume
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": rook-ceph
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": kube-system
+            k8s-app: kube-dns
+      toPorts:
+        - ports:
+            - port: "53"
+              protocol: UDP
+    # host/remote-node: kubelet probes + node-local reach (Ceph mons on host net)
+    # world: image pulls and llama-server's Hugging Face model download
+    - toEntities:
+        - host
+        - remote-node
+        - world
+    # ICMP — egress mirror; see ingress block above.
+    - toEndpoints:
+        - matchExpressions:
+            - key: io.kubernetes.pod.namespace
+              operator: Exists
+      icmps:
+        - fields:
+            - { type: 0,   family: IPv4 }
+            - { type: 3,   family: IPv4 }
+            - { type: 8,   family: IPv4 }
+            - { type: 11,  family: IPv4 }
+            - { type: 12,  family: IPv4 }
+            - { type: 1,   family: IPv6 }
+            - { type: 2,   family: IPv6 }
+            - { type: 3,   family: IPv6 }
+            - { type: 4,   family: IPv6 }
+            - { type: 128, family: IPv6 }
+            - { type: 129, family: IPv6 }
+            - { type: 133, family: IPv6 }
+            - { type: 134, family: IPv6 }
+            - { type: 135, family: IPv6 }
+            - { type: 136, family: IPv6 }
+            - { type: 143, family: IPv6 }

kubernetes/infrastructure/network-policies/kustomization.yaml

@@ -24,4 +24,5 @@ resources:
   - woodpecker-ns.yaml
   - trmnl-ns.yaml
   - wordpress-ns.yaml
+  - ai-ns.yaml
   - allow-cilium-health.yaml