tune(gpu1): grow selene 0.13→0.17 + qwen36 0.32→0.34 into the buffer

Put GPU1's idle ~11 GB buffer to work on the two KV-bound models that gained
live consumers from the worldtree migration (granite + the pooling models
under-use their util, so growing them is wasted):
- selene 0.13→0.17: KV 2.53→6.33 GiB, concurrency 1.27x→3.16x @32K (Domari judge)
- qwen36 0.32→0.34: KV 7.73→9.63 GiB, concurrency 2.92x→3.64x @131K (arbo judge +
  worldtree actor/echo + gateway)
GPU1 free now ~5.6 GB (safe floor for single-service recreates).

stacks/qwen36-vl/.env.example

@@ -22,9 +22,11 @@ QWEN_GPU_ID=1
 # fp16 KV + CUDA-graph. fp16 KV (compose drops --kv-cache-dtype fp8): the NVFP4
 # swap freed enough room to run full-precision KV. Hybrid attn (10/40 full-attn)
 # keeps even fp16 KV cheap. GPU-1 budget (2026-06-15 rebalance, pinned): qwen36
-# 0.32 + granite 0.34/131072 (RESTORED from the FP8-era 0.24/64K) + trio 0.16 =
-# ~0.82, ~24 GB free headroom. Recreate ONE service at a time (profiling race).
-QWEN_GPU_MEM_UTIL=0.32
+# GPU-1 budget (2026-06-16): qwen36 0.34 (grown from 0.32 for the arbo-judge +
+# worldtree actor/echo + gateway load) + granite 0.34/131072 + selene 0.17 + trio
+# 0.16. Nominal sum >1.0 but the pooling models + granite under-use their util, so
+# it fits with ~5-6 GB physical free. Recreate ONE service at a time (profiling race).
+QWEN_GPU_MEM_UTIL=0.34
 QWEN_MAX_MODEL_LEN=131072
 # Sampler-warmup OOM guard on the shared GPU (248K vocab × default 1024 seqs is
 # a huge transient). 32 is plenty for a vision endpoint.