diff --git a/k8s/apps/llm-serving/coder.yaml b/k8s/apps/llm-serving/coder.yaml
new file mode 100644
index 0000000..8b1b05f
--- /dev/null
+++ b/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
diff --git a/k8s/apps/llm-serving/kustomization.yaml b/k8s/apps/llm-serving/kustomization.yaml
index 8f0fb81..5680790 100644
--- a/k8s/apps/llm-serving/kustomization.yaml
+++ b/k8s/apps/llm-serving/kustomization.yaml
@@ -14,6 +14,10 @@ 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
   # ornith is retired: its InferenceService still reports Ready=True while running zero
   # replicas, because desired equals available at 0. Its manifest was never committed,