hold clef-candle at replicas 0 until the image carries the GDN fix

candle-vllm PR #5 (the conv fix) could not be merged, so CI has not published
a :sm70 image containing it. Deploying clef against an older :sm70 fails
silently: the delta rule returns zero on all 24 linear-attention layers and
/v1/systemone still answers 200 with well-formed but wrong classifications.

Raise to 1 once a :sm70 build from the fix merge exists.

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

@@ -0,0 +1,85 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: clef-candle
+  namespace: llm-serving
+  labels:
+    app.kubernetes.io/part-of: llm-serving
+    app: clef-candle
+spec:
+  # HOLD AT 0 until candle-vllm:sm70 contains the GDN conv fix (candle-vllm PR #5).
+  # An older :sm70 zeroes the key channel band, starves the delta rule and makes
+  # every linear-attention layer return zero. The response is still HTTP 200 and
+  # well formed, so the failure is silent. Raise to 1 only after CI publishes a
+  # :sm70 build from the merge that carries the fix.
+  replicas: 0
+  strategy:
+    # One replica holds one GPU, so Recreate keeps the rollout to a single
+    # weights load instead of needing a second card for a surge pod.
+    type: Recreate
+  selector:
+    matchLabels:
+      app: clef-candle
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/part-of: llm-serving
+        # The clef-serve Service now selects this label (see clef-serve.yaml),
+        # so clients keep addressing clef-serve:80 unchanged. The reference
+        # pytorch Deployment must stay at replicas 0 while this runs, or the
+        # Service splits traffic across two different implementations.
+        app: clef-candle
+    spec:
+      # Required. Without the nvidia runtime class libcuda.so is never injected
+      # and candle-vllm panics in cudarc at startup.
+      runtimeClassName: nvidia
+      containers:
+        - name: candle
+          # :sm70 is built by .gitea/workflows/candle-vllm-sm70.yaml on every
+          # merge to main. It MUST be a build that contains the GDN conv fix
+          # (candle-vllm PR: feat/clef-systemone). Older :sm70 images zero the
+          # key channel band in causal_conv1d_fwd, which starves the delta rule
+          # and makes every linear-attention layer return zero. The symptom is
+          # a well-formed /v1/systemone response with wrong answers, not an error.
+          image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70
+          command: ["/usr/local/bin/candle-vllm"]
+          # clef-flash is a hybrid Qwen3_5 model: 8 full-attention layers plus 24
+          # GDN layers that carry per-sequence recurrent state. It runs on ONE
+          # card because the state is not shardable the way attention KV is.
+          args:
+            - --w
+            - /mnt/models/clef-flash
+            - --d
+            - "0"
+            - --dtype
+            - f16
+            - --p
+            - "8080"
+            - --max-num-seqs
+            - "4"
+            # About 66.8K KV tokens. Clef is a single-shot classifier-style
+            # endpoint, so it does not need the coder's pool.
+            - --kv-fraction
+            - "0.30"
+            # Prefix caching stays on: clef requests repeat the schema block.
+            - --prefill-chunk-size
+            - "1024"
+          ports:
+            - containerPort: 8080
+          resources:
+            limits:
+              cpu: "8"
+              memory: 32Gi
+              nvidia.com/gpu: "1"
+            requests:
+              cpu: "4"
+              memory: 16Gi
+              nvidia.com/gpu: "1"
+          volumeMounts:
+            - name: models
+              mountPath: /mnt/models
+              readOnly: true
+      volumes:
+        - name: models
+          persistentVolumeClaim:
+            claimName: llm-models

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:
@@ -129,8 +127,11 @@ metadata:
   labels:
     app.kubernetes.io/part-of: llm-serving
 spec:
+  # Points at the candle-vllm implementation (clef-candle.yaml). Switching back
+  # to the reference pytorch server is this line plus that Deployment's replicas;
+  # no client changes, because the Service name and ClusterIP do not move.
   selector:
-    app: clef-serve
+    app: clef-candle
   ports:
   - name: http
     port: 80

k8s/apps/llm-serving/coder-14b-fp16.yaml

@@ -0,0 +1,106 @@
+# Qwen2.5-Coder-14B-Instruct, fp16, tensor-parallel across two V100s.
+#
+# Why this model and this precision. The previous workload was Qwen3-Coder-30B-A3B
+# in GPTQ W4A16 on one V100. Profiling that deployment showed SM utilisation at
+# 98% while the memory controller sat at 19%: the V100 has FP16 tensor cores and
+# no INT4 ones, so every decode step ran a dequantisation pass on the SM. That is
+# a hardware-generation mismatch, not a tunable. Sequence throughput matches the
+# arithmetic: at batch 1 the step cost equalled a full-model read (17.6 ms against
+# 15 GB / 900 GB/s = 16.7 ms) while the routed experts alone are ~1.5 GB.
+#
+# Measured on this hardware, same harness, same 16-stream load:
+#
+#   GPTQ W4A16 30B-A3B, TP=1  : ITL 71.5 ms, 215 tok/s aggregate
+#   fp16 14B, TP=2            : ITL 39.7 ms, 287 tok/s aggregate
+#
+# The dense model degrades only 1.4x from 1 to 16 streams (27.9 -> 39.7 ms) while
+# the MoE degrades 4.1x (17.6 -> 71.5 ms), because a dense model reads its weights
+# once per step and amortises across the batch. The MoE pays expert weight traffic
+# per sequence. Single-stream latency is the one case where the MoE still wins
+# (17.6 ms against 27.9 ms), since it activates ~3B parameters per token.
+#
+# TP=2 notes that cost time to establish, so they are recorded here:
+#   - Sharding needs no code change. Every dimension divides by 2: attention heads
+#     40 -> 20, KV heads 8 -> 4, intermediate_size 13824 -> 6912, vocab
+#     152064 -> 76032. Verify this first for any replacement model.
+#   - TP=2 on a GPTQ checkpoint is what crashed (CUDA_ERROR_ILLEGAL_ADDRESS in
+#     forward()) on the previous model. The cause was quantised weight sharding
+#     and the g_idx guard in src/openai/models/linear.rs, not TP itself. A dense
+#     fp16 checkpoint has no g_idx, no packed weights and no scales, so it takes
+#     the plain column/row-parallel path and the crash does not reproduce.
+#   - These are V100-PCIE cards with no NVLink, so every layer's all-reduce
+#     crosses PCIe. It shows up in TTFT (275 ms at 1 stream, 1090 ms at 16)
+#     more than in ITL.
+#   - KV capacity is 11635 MB per rank, about 59K context tokens. The fp16
+#     weights consume the room the KV pool would otherwise use. This is well
+#     short of the 256K the previous model targeted, and it is the price of
+#     leaving the dequantisation path.
+#
+# Weights must be in the PVC as /mnt/models/coder-14b-fp16 (28 GB, 6 shards).
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: candle-vllm
+  namespace: llm-serving
+  labels:
+    app.kubernetes.io/part-of: llm-serving
+    app: candle-vllm
+spec:
+  replicas: 1
+  strategy:
+    # One replica holds both GPUs, so a rolling update would need a second pair.
+    # Recreate keeps the rollout to a single weights load.
+    type: Recreate
+  selector:
+    matchLabels:
+      app: candle-vllm
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/part-of: llm-serving
+        app: candle-vllm
+    spec:
+      # Required. Without this the container runs under runc, libcuda.so is never
+      # injected, and candle-vllm panics in cudarc at startup with
+      # "Unable to dynamically load the cuda shared library".
+      runtimeClassName: nvidia
+      containers:
+        - name: candle
+          image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70-gptq-slim
+          command: ["/usr/local/bin/candle-vllm"]
+          # fp16, not GPTQ, and two ranks, not one. See the header for the
+          # measurements and the TP=2 findings.
+          args:
+            - --w
+            - /mnt/models/coder-14b-fp16
+            - --d
+            - 0,1
+            - --dtype
+            - f16
+            - --p
+            - "8080"
+            - --max-num-seqs
+            - "32"
+            # 11.6 GB of KV per rank, about 59K context tokens. The fp16 weights
+            # leave no room for more.
+            - --kv-fraction
+            - "0.85"
+          ports:
+            - containerPort: 8080
+          resources:
+            limits:
+              cpu: "8"
+              memory: 36Gi
+              nvidia.com/gpu: "2"
+            requests:
+              cpu: "4"
+              memory: 12Gi
+              nvidia.com/gpu: "2"
+          volumeMounts:
+            - name: models
+              mountPath: /mnt/models
+              readOnly: true
+      volumes:
+        - name: models
+          persistentVolumeClaim:
+            claimName: llm-models

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

@@ -13,9 +13,23 @@ resources:
   # clef-flash runs through clef-serve.yaml, not through an SGLang runtime. SGLang needs
   # 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.
+  # coder-14b-fp16.yaml owns the `candle-vllm` Deployment name and the
+  # `app: candle-vllm` label that coder-service.yaml selects. It must be the only
+  # workload with that label, or the Service splits traffic across two models.
+  #
+  # fp16 on two V100s, not GPTQ on one: measured 39.7 ms ITL and 287 tok/s at 16
+  # streams against 71.5 ms and 215 tok/s for the 4-bit MoE. The V100 has FP16
+  # tensor cores and no INT4 ones, so 4-bit pays dequantisation on the SM while
+  # the memory controller idles at 19%.
+  # clef-candle.yaml serves clef-flash on candle-vllm (hybrid Qwen3_5, one GPU)
+  # and owns the clef-candle label that clef-serve.yaml's Service now selects.
+  # It must not sync against an image built before the GDN conv fix.
+  - clef-candle.yaml
+  - coder-14b-fp16.yaml
   - clef-serve.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,