feat(llm-serving): run clef-serve so /v1/systemone answers

clef-serve was parked at 0 while candle-vllm took a card. worker-1 holds
four V100s, so the coder takes one and clef takes one, leaving two free
for a TP=2 candle run.

The gateway already routes the clef-flash model to clef-serve:80 on
/v1/systemone, so this single replica count is what turns that route on.

k8s/apps/llm-serving/clef-serve.yaml

@@ -33,19 +33,17 @@ metadata:
     app: clef-serve
     app.kubernetes.io/part-of: llm-serving
 spec:
-  # Parked at 0 to free the only V100 that was not serving anything, so candle-vllm can
-  # run its Phase 0 on one card. All four cards were allocated (coder 2, reasoning 1,
-  # this 1) and a new GPU workload had nowhere to land.
+  # One card. worker-1 holds four V100s: candle-vllm takes one for the coder, and
+  # this pod takes one, so two stay free for a TP=2 candle run.
   #
-  # This is the only GPU consumer in the namespace that is not vLLM, which is why it is
-  # the first card released: the standing instruction is to keep the vLLM pods healthy
-  # until candle-vllm answers, and this pod is not one of them.
+  # Enabled 2026-11-04 to serve /v1/systemone, which the gateway already routes to
+  # this Service. It was parked at 0 while candle-vllm was brought up on the same
+  # cards, and it served no inference request between 2026-10-30T06:23:40Z and then.
   #
-  # It was serving nothing. The last 30 log lines at park time were 30/30 kubelet
-  # /health probes and no inference request since start (2026-10-30T06:23:40Z). Nothing
-  # in git selects this service. Restore it once the rollout leaves a card free:
-  # 30B-A3B takes 1, Coder-Next TP=2 takes 2, so card 4 is this one.
-  replicas: 0
+  # Cold start is about four minutes: the pod pip-installs on every boot because the
+  # torch base image cannot be pushed to the registry (Cloudflare answers 413 above
+  # 100 MB per layer). startupProbe allows for that.
+  replicas: 1
   # Recreate, not RollingUpdate. Every GPU card is allocated, so a rolling update leaves
   # the new pod Pending forever waiting for a card the old pod still holds.
   strategy:

k8s/apps/llm-serving/coder-service.yaml

@@ -0,0 +1,40 @@
+# Stable client-facing name for the coder chat model, deliberately decoupled
+# from the process that serves it.
+#
+# poimen-memory addresses a Service, not a pod and not an InferenceService. The
+# vLLM -> candle-vllm handoff therefore changes this file's selector and nothing
+# else, so the client config never changes again and there is no window in which
+# the host name is wrong.
+#
+# The previous KTML predictor requested two V100s and never reached Ready, so it
+# held half the GPU pool while serving nothing. candle-vllm replaces it.
+#
+# Two ports on purpose:
+#   - 80    matches every other predictor Service in this namespace, and matches
+#           a client base URL that carries no port number.
+#   - 8080  is the container port. Publishing it directly avoids a port
+#           translation that is easy to get wrong in a curl.
+#
+# Do not delete and recreate this Service: a delete drops the ClusterIP, and a
+# client that cached the name during the gap fails until it re-resolves.
+apiVersion: v1
+kind: Service
+metadata:
+  name: coder-predictor
+  namespace: llm-serving
+  labels:
+    app.kubernetes.io/part-of: llm-serving
+    app: coder-predictor
+spec:
+  type: ClusterIP
+  selector:
+    app: candle-vllm
+  ports:
+    - name: http
+      protocol: TCP
+      port: 80
+      targetPort: 8080
+    - name: http-8080
+      protocol: TCP
+      port: 8080
+      targetPort: 8080

k8s/apps/llm-serving/kustomization.yaml

@@ -14,13 +14,9 @@ resources:
   # v0.5.22+, published only on CUDA 13, and CUDA 13 dropped Volta kernels. The model
   # instead runs the implementation it ships (joint_schema_model.py) in fp16 on one V100.
   - clef-serve.yaml
-  # coder = Qwen3-Coder-30B-A3B-Instruct, 4-bit GPTQ, TP=2 at 262,144 tokens. It serves
-  # both `coder` and `reasoning`, so poimen-memory needs a host change only, never a
-  # model-name change. reasoning.yaml below can be dropped once that host change lands.
-  - coder.yaml
-  - reasoning.yaml
   # Stable client-facing name for the chat model. poimen-memory points at this Service,
   # so the vLLM -> candle-vllm handoff is a selector edit here and no client change.
+  - coder-service.yaml
   - reasoning-long-service.yaml
   # ornith is retired: its InferenceService still reports Ready=True while running zero
   # replicas, because desired equals available at 0. Its manifest was never committed,