k8s/apps/llm-serving/reasoning.yaml

@@ -30,7 +30,28 @@ spec:
       # 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
+      # Two served names, one pod, on purpose. `reasoning-long` is the name the goal map
+      # uses for this model, and the name qwen-finetune wiring targets. It is NOT a
+      # separate InferenceService: this model already occupies the only free card, and a
+      # second pod on the same weights cannot schedule (4 of 4 GPUs allocated: coder 2,
+      # this 1, clef-serve 1). Serving both names from one process costs nothing and
+      # keeps the two callers working without a second weights copy.
+      #
+      # `reasoning` is deliberately still served here. poimen-memory pins
+      # LLM_CHAT_BASE=reasoning-predictor, so dropping the name here breaks ingest
+      # immediately. coder-predictor also serves `reasoning`; that ambiguity is known
+      # and documented in the PR. Resolve it by moving the client host, not by
+      # removing the name here.
+      #
+      # The two names are separate list entries, NOT one comma-joined value. This
+      # image's parser returns ['reasoning,reasoning-long'] for the comma form, i.e. a
+      # single model whose name contains a comma, and api_server.py then iterates
+      # args.served_model_name to build the model list. Verified against vLLM 0.11.0:
+      #   FlexibleArgumentParser().parse_args([... "--served-model-name", "a", "b"])
+      #     -> ['a', 'b']
+      - --served-model-name
+      - reasoning
+      - reasoning-long
       - --quantization=gptq
       - --dtype=float16
       - --kv-cache-dtype=auto