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/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,