diff --git a/k8s/apps/llm-serving/kustomization.yaml b/k8s/apps/llm-serving/kustomization.yaml
index 31d77a3..92308fd 100644
--- a/k8s/apps/llm-serving/kustomization.yaml
+++ b/k8s/apps/llm-serving/kustomization.yaml
@@ -26,12 +26,10 @@ resources:
   #
   # Re-add it when the model gets an Ampere-or-newer GPU. The manifest itself is
   # kept in git (k8s/apps/llm-serving/clef-flash.yaml) with the same reasoning.
-  # ornith and reasoning are live but unmanaged: their manifests were never committed
-  # to this directory, so listing them made `kustomize build` fail and left the whole
-  # Application in ComparisonError. Remove the entries rather than the models. The
-  # Application runs with prune=false, so an object that is no longer in the desired
-  # state is left alone, not deleted. Re-export both from live state when they are
-  # next edited.
+  - 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,
+  # so it is left unmanaged and is not listed here. Delete the CR when convenient.
   - embeddings.yaml
   - reranker.yaml
   - qwen-cpu.yaml
diff --git a/k8s/apps/llm-serving/reasoning.yaml b/k8s/apps/llm-serving/reasoning.yaml
new file mode 100644
index 0000000..77f5203
--- /dev/null
+++ b/k8s/apps/llm-serving/reasoning.yaml
@@ -0,0 +1,102 @@
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: reasoning
+  namespace: llm-serving
+  labels:
+    app.kubernetes.io/name: llm-reasoning
+    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
+    # Every GPU card is allocated, so a rolling update deadlocks: the new pod needs a
+    # card the old pod still holds and sits Pending forever. Recreate frees the card
+    # first. The cost is a short gap in service for this model only.
+    deploymentStrategy:
+      type: Recreate
+    containers:
+    - name: kserve-container
+      image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
+      args:
+      # Qwen3-30B-A3B is a 30.5B MoE with 3.3B active per token. On a V100 that matters:
+      # the 32B dense model it replaces needed two cards for the same context, this needs
+      # one, because its KV cache is 96 KiB/token against the dense model's 256 KiB.
+      #
+      # Measured on this cluster, one V100-32GB, 64k context: 15.55 GiB of weights,
+      # 137k tokens of KV cache, 2.09x concurrency at 65536 tokens per request.
+      - --model=Qwen/Qwen3-30B-A3B-GPTQ-Int4
+      - --served-model-name=reasoning
+      - --quantization=gptq
+      - --dtype=float16
+      - --kv-cache-dtype=auto
+      # Native window is 40960. YaRN factor 2.0 reaches 81920, so 64k is inside it.
+      - --rope-scaling={"rope_type":"yarn","factor":2.0,"original_max_position_embeddings":40960}
+      - --tensor-parallel-size=1
+      - --pipeline-parallel-size=1
+      - --max-model-len=65536
+      - --gpu-memory-utilization=0.90
+      - --max-num-seqs=8
+      - --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:
+      # Both are required on this hardware, not tuning:
+      #  - flashinfer's check_cuda_arch raises AttributeError on compute capability 7.0
+      #    (flashinfer/jit/core.py:55, minor.isdigit() on an int), so the sampler must stay off
+      #  - FlashInfer and FlashAttention-2 both need sm75+, so the Triton backend 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
+        # Weight load took 18s from cache and 148s on a cold download, and the first
+        # torch.compile adds about 70s. 80 x 15s leaves ample room for a cold start.
+        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