feat(llm-serving): add coder, serving Qwen3-Coder-30B-A3B on one V100

The name now matches the model. `coder` serves
Qwen3-Coder-30B-A3B-Instruct, a 30.5B MoE with 3.3B active, quantized to
4-bit GPTQ. It is measured working on this node, not assumed.

Measured, on worker-1, driver 580.159.04, compute capability 7.0:

  GET  /v1/models        -> id=coder, root=ModelCloud/Qwen3-Coder-30B-A3B-Instruct-GPTQMODEL-W4A16-A
  POST /v1/chat/completions model=coder -> 200 in 3.18s,
       "fn main() { println!(\"hi\"); }"
  POST /v1/chat/completions model=reasoning -> 200 in 0.21s

Weights loaded in 15.61 GiB. torch.compile took 78s. The pod reached
1/1 Ready. `kubectl diff -f coder.yaml` returns empty, so the manifest
matches live state.

Two names are served on purpose: `coder` and `reasoning`. The rename
therefore needs no change to poimen-memory's LLM_MODEL, only its host.
That keeps the client working through the cutover.

Why not Qwen3-Coder-Next, the model in the original plan. It cannot run
here, and that is measured too. A v0.15.0 image on this free V100 gave:

  TORCH 2.9.1+cu129
  ARCH_LIST []                       <- empty
  RuntimeError: cudaGetDeviceCount() ... Error 803
  fla: ModuleNotFoundError           <- no GatedDeltaNet kernels

vLLM 0.15.0 requires compute capability 7.5 or higher and fails here
before it loads a model. Qwen3-Next's hybrid attention needs the `fla`
module, which the image does not ship. So this stays on the pinned
v0.11.0 image, which reports sm_70..sm_120 and works.

Qwen publishes this model as BF16 (61.1 GB) and FP8 only. V100 has no
FP8, and 61.1 GB does not fit 32 GB, so 4-bit GPTQ is the only form that
fits one card. The checkpoint is a community quant; the manifest says so
and names the reason.

reasoning.yaml stays listed until poimen-memory's LLM_CHAT_BASE moves
from reasoning-predictor to coder-predictor. Two GPUs are free, so coder
rolled out beside reasoning with no downtime.

k8s/apps/llm-serving/coder.yaml

@@ -0,0 +1,118 @@
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: coder
+  namespace: llm-serving
+  labels:
+    app.kubernetes.io/name: llm-coder
+    app.kubernetes.io/part-of: llm-serving
+  annotations:
+    serving.kserve.io/deploymentMode: RawDeployment
+spec:
+  predictor:
+    minReplicas: 1
+    maxReplicas: 1
+    nodeSelector:
+      kubernetes.io/hostname: worker-1
+    # Two GPUs are free, so this rolls out beside `reasoning` instead of over it.
+    # Recreate still matters for the later swap: a rolling update needs a card the
+    # old pod holds and would sit Pending forever.
+    deploymentStrategy:
+      type: Recreate
+    containers:
+    - name: kserve-container
+      # Pinned to v0.11.0 on purpose. This is the last image measured to work on this
+      # node: torch 2.8.0+cu128, arch list sm_70..sm_120, driver 580.159.04.
+      # v0.15.0 was measured and FAILED here: arch list empty, and
+      # `cudaGetDeviceCount()` raised Error 803 (unsupported driver combination)
+      # before any model load. Do not raise this tag without re-running that probe.
+      image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
+      args:
+      # Qwen3-Coder-30B-A3B-Instruct: 30.5B total, 3.3B active. Architecture is
+      # Qwen3MoeForCausalLM, the same class as the Qwen3-30B-A3B already serving
+      # here, so it takes the proven code path on sm70.
+      #
+      # Qwen ships this model only as BF16 (61.1 GB) and FP8 (no FP8 on Volta).
+      # 4-bit GPTQ is the only form that fits one 32 GB card. This checkpoint is a
+      # community quant: ModelCloud maintains GPTQModel, and the repo is complete
+      # (config + tokenizer + 4 shards, 16.9 GB). Verified by loading, not by trust.
+      #
+      # Qwen3-Coder-Next (80B/3B, the hybrid-attention model) is NOT an option:
+      # its GatedDeltaNet kernels need compute capability 7.5+ and the `fla` module,
+      # and sm70 has neither. Measured, not assumed.
+      - --model=ModelCloud/Qwen3-Coder-30B-A3B-Instruct-GPTQMODEL-W4A16-A
+      # `reasoning` stays a served name so an existing client that sends
+      # model=reasoning keeps working after the DNS rename. vLLM takes the names
+      # as a space-separated list, so they go in as separate argv items.
+      - --served-model-name
+      - coder
+      - reasoning
+      - --quantization=gptq
+      - --dtype=float16
+      - --kv-cache-dtype=auto
+      # Native window is 262144. 64k keeps the KV cache comfortable on one card;
+      # raise only after measuring.
+      - --max-model-len=65536
+      - --tensor-parallel-size=1
+      - --pipeline-parallel-size=1
+      - --gpu-memory-utilization=0.90
+      - --max-num-seqs=16
+      - --max-num-batched-tokens=8192
+      - --long-prefill-token-threshold=2048
+      - --enable-chunked-prefill
+      - --enable-prefix-caching
+      - --reasoning-parser=qwen3
+      - --enable-auto-tool-choice
+      - --tool-call-parser=hermes
+      - --host=0.0.0.0
+      - --port=8080
+      env:
+      # Required on this hardware, not tuning:
+      #  - flashinfer's check_cuda_arch raises on compute capability 7.0,
+      #    so the sampler must stay off
+      #  - FlashInfer and FlashAttention-2 need sm75+, so Triton serves attention
+      - name: VLLM_USE_FLASHINFER_SAMPLER
+        value: "0"
+      - name: VLLM_ATTENTION_BACKEND
+        value: TRITON_ATTN
+      - name: HF_HOME
+        value: /mnt/models
+      ports:
+      - containerPort: 8080
+        protocol: TCP
+      readinessProbe:
+        httpGet:
+          path: /health
+          port: 8080
+        periodSeconds: 10
+      startupProbe:
+        httpGet:
+          path: /health
+          port: 8080
+        # The coder checkpoint is a 16.9 GB cold download on first start, then
+        # ~20 s of weight load and ~80 s of torch.compile. 80 x 15 s covers it.
+        failureThreshold: 80
+        periodSeconds: 15
+      resources:
+        requests:
+          cpu: "4"
+          memory: 12Gi
+          nvidia.com/gpu: "1"
+        limits:
+          cpu: "8"
+          memory: 36Gi
+          nvidia.com/gpu: "1"
+      volumeMounts:
+      - mountPath: /mnt/models
+        name: models
+      - mountPath: /dev/shm
+        name: shm
+    runtimeClassName: nvidia
+    volumes:
+    - name: models
+      persistentVolumeClaim:
+        claimName: llm-models
+    - name: shm
+      emptyDir:
+        medium: Memory
+        sizeLimit: 2Gi

k8s/apps/llm-serving/kustomization.yaml

@@ -10,8 +10,12 @@ kind: Kustomization
 # way after any edit here: a GPU predictor restart is a weights reload measured
 # in tens of seconds, not a rolling update.
 resources:
+  - coder.yaml
   - embeddings.yaml
   - ornith.yaml
+  # reasoning stays listed only until poimen-memory's LLM_CHAT_BASE moves to
+  # coder-predictor. coder already serves the name `reasoning`, so the client
+  # needs no model-name change, only the host change. Drop this line after that.
   - reasoning.yaml
   - reranker.yaml
   - qwen-cpu.yaml