fix(intern-decision-serve): load and score on one dedicated inference thread

torch keeps CUDA state per host thread (cuBLAS handles and workspaces), partly outside the
per-process VRAM cap. Scoring on anyio's threadpool let 40 threads each create it: measured on
fv-ml1 GPU 3, +252 MiB outside the cap and +326 MiB inside, which pushed the process past the
10,300 MiB GPU 1 budget. Load, warm-up and every call now run on the same single thread.

services/intern-decision-serve/tests/test_main.py

@@ -21,3 +21,28 @@ def test_app_from_env_goes_offline_before_the_engine_loads(monkeypatch):
     app = main.app_from_env()
     assert seen == {"offline": ("1", "1"), "token": "k" * 40}
     assert app.title == "intern-decision-serve"
+
+
+def test_the_engine_loads_on_the_same_thread_every_call_later_runs_on(monkeypatch):
+    import threading
+    from intern_decision_serve import engine as engine_module
+    from intern_decision_serve import main
+    from fastapi.testclient import TestClient
+    seen = {}
+
+    class Recording(FakeEngine):
+        def predict(self, request):
+            seen.setdefault("calls", set()).add(threading.get_ident())
+            return super().predict(request)
+
+    def fake_load(settings, **_kw):
+        seen["load"] = threading.get_ident()
+        return Recording()
+
+    monkeypatch.setenv("INTERN_DECISION_API_TOKEN", "k" * 40)
+    monkeypatch.setattr(engine_module.TorchEngine, "load", staticmethod(fake_load))
+    client = TestClient(main.app_from_env())
+    body = {"id": "r", "state": "s", "question": "q?", "options": [{"id": "a", "description": "A"},
+                                                                  {"id": "b", "description": "B"}]}
+    assert client.post("/decide", json=body, headers={"Authorization": "Bearer " + "k" * 40}).status_code == 200
+    assert seen["calls"] == {seen["load"]} and seen["load"] != threading.get_ident()