Initial commit: router front-door vLLM stack

Three always-running vLLM services (text TP=2 GPU0+1, ocr + embed on GPU2,
sleep mode) behind a FastAPI router that auto-wakes models on request.
Tiered idle (sleep 15 min / offload 3 h), depth-aware 503s with
Retry-After, persisted wake-intent recovery, admin API on 127.0.0.1:8010.
Routine control via vllmctl is pure HTTP — no docker on the request path.

Verified: 91 router unit tests + 15-test E2E on real hardware
(measurements in CALIBRATION.md; design record in
.claude/memory/router-front-door-plan.md).

Old nginx stack files removed before git init; design survives in
.claude/memory/sleep-mode-implementation-plan.md.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

diag/gpu_integrity.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+"""Per-GPU basic integrity: H2D/D2H copy fidelity, matmul correctness,
+repeated-copy stress. Does NOT test P2P — use p2p_check.py for that.
+
+Run:  docker exec vllm python3 /path/to/gpu_integrity.py
+"""
+import torch
+
+print("torch", torch.__version__, "cuda", torch.version.cuda, "devs", torch.cuda.device_count())
+for dev in range(torch.cuda.device_count()):
+    try:
+        torch.cuda.set_device(dev)
+        x = torch.randint(0, 2**31 - 1, (128 * 1024 * 1024,), dtype=torch.int32)
+        y = x.to(f"cuda:{dev}")
+        z = y.cpu()
+        copy_ok = torch.equal(x, z)
+        a = torch.randn(2048, 2048, dtype=torch.float32)
+        b = torch.randn(2048, 2048, dtype=torch.float32)
+        ref = (a.double() @ b.double()).float()
+        c = (a.to(f"cuda:{dev}") @ b.to(f"cuda:{dev}")).cpu()
+        err = (c - ref).abs().max().item()
+        stress_ok = True
+        for _ in range(20):
+            s = torch.randint(0, 2**31 - 1, (16 * 1024 * 1024,), dtype=torch.int32)
+            if not torch.equal(s, s.to(f"cuda:{dev}").cpu()):
+                stress_ok = False
+                break
+        print(f"GPU{dev}: copy_ok={copy_ok} matmul_max_err={err:.3e} "
+              f"stress_ok={stress_ok} name={torch.cuda.get_device_name(dev)}")
+        del x, y, z, a, b, c
+        torch.cuda.empty_cache()
+    except Exception as e:
+        print(f"GPU{dev}: ERROR {type(e).__name__}: {e}")