diff --git a/Makefile b/Makefile
index 547dfb9..6379082 100644
--- a/Makefile
+++ b/Makefile
@@ -36,7 +36,7 @@ SEARCH_Q     ?= computer
         prometheus-trigger-probe bench-5f-threshold-calibration \
         bench-5f-selfmodel-snapshot bench-5f-finetuning-shardchain \
         bench-5f-falsification-hard bench-fork-baseline-hard bench-5f-formulate-hard \
-        bootstrap-math bootstrap-nli bench-nli-shadow export-nli-onnx clean clean-db clean-data help \
+        bootstrap-math bootstrap-nli bootstrap-nli-only bench-nli-shadow export-nli-onnx clean clean-db clean-data help \
         textbooks-summary textbooks-urls fetch-textbooks textbooks-stats textbooks-verify \
         crawl-textbooks crawl-textbooks-stats textbook textbook-list
 
@@ -789,6 +789,20 @@ bootstrap-nli: bootstrap ## install [nli] extras + warm the pinned NLI checkpoin
 	$(PIP) install -e '.[nli]'
 	$(PY) -c "from arborist.qa.nli import ShadowNLI; r=ShadowNLI(); r._ensure_loaded(); print('nli runtime ·', 'available:', r.available, '· backend:', r.backend, '· device:', r.device, '·', r._reason)"
 
+# #000049 §7 #22 — lean NLI compute-node bootstrap: a venv + `[nli]` ONLY
+# (no `[dev]` extras — no crawler/hessian/vec/sympy). For a GPU box that
+# just runs NLI sweeps: on a CUDA host PyPI's torch wheel is the CUDA
+# build, so `ShadowNLI` auto-detects cuda (ARBORIST_NLI_DEVICE overrides)
+# and `make bench-nli-shadow` / `make export-nli-onnx` / the candidate
+# bench all run on the GPU with no further wiring. Measured: an 82M
+# cross-encoder, batched, on a 4090 ≈ 0.09 ms/pair (~350x onnx-int8-cpu,
+# ~1300x torch-cpu-batch1) → a full bench-qa shadow sweep is <1 s of NLI.
+bootstrap-nli-only: ## #000049 §7 #22 — minimal venv + [nli] only (GPU/CPU compute-node setup; no [dev])
+	[ -d $(VENV) ] || $(PYTHON) -m venv $(VENV)
+	$(PIP) install -q -U pip
+	$(PIP) install -e '.[nli]' pytest
+	$(PY) -c "import torch; from arborist.qa.nli import ShadowNLI; r=ShadowNLI(); r._ensure_loaded(); print('torch', torch.__version__, '· cuda', torch.cuda.is_available(), (torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')); print('nli runtime ·', 'available:', r.available, '· backend:', r.backend, '· device:', r.device, '·', r._reason)"
+
 # #000049 §3 speedup — export the pinned shadow-NLI checkpoint to ONNX
 # (+ int8 dynamic quantization), into ~/.arborist/models/nli/<ver>/onnx/.
 # ShadowNLI._ensure_loaded auto-prefers the export if present (~2-4x on
diff --git a/docs/tickets/ticket-000049-attribution-aware-grounding-check.md b/docs/tickets/ticket-000049-attribution-aware-grounding-check.md
index 31dbd9e..ef656a4 100644
--- a/docs/tickets/ticket-000049-attribution-aware-grounding-check.md
+++ b/docs/tickets/ticket-000049-attribution-aware-grounding-check.md
@@ -844,10 +844,22 @@ state, **not** committed — same discipline as the textbook manifest /
 `[vec]`), and `_ensure_loaded` loads `model_quantized.onnx` via
 `optimum.onnxruntime` (backend `onnx-int8`), falling back silently to
 torch when no export / no `optimum`. Measured: torch-cpu-batch1 ≈ 120
-ms/pair → **onnx-int8-cpu-batched ≈ 32 ms/pair** (~4×); on a CUDA box
-(the 4090) batched inference is ~10⁴–10⁵ pairs/s — the whole sweep is
-seconds. `optimum[onnxruntime]` added to the `[nli]` extra; 24 tests in
-`tests/test_nli_shadow.py`.
+ms/pair → **onnx-int8-cpu-batched ≈ 32 ms/pair** (~4×) → **torch-cuda-
+batched ≈ 0.09 ms/pair on a 4090** (512 pairs in 0.047 s — ~350×
+onnx-int8-cpu, ~1300× the original), so a full `bench-qa` n=3 shadow
+sweep (~8 k clause-pairs) is **< 1 s** of NLI compute on the GPU
+(2.4 s wall for a 62-record synthetic sweep, model load included).
+`optimum[onnxruntime]` added to the `[nli]` extra; 24 tests in
+`tests/test_nli_shadow.py`. **GPU compute-node workflow** (verified on
+`ai`, RTX 4090): `make bootstrap-nli-only` (a venv + `[nli]` only — no
+`[dev]` bloat; PyPI's torch wheel is the CUDA build on a CUDA host) →
+`ShadowNLI` auto-detects cuda (`ARBORIST_NLI_DEVICE` overrides) → `make
+bench-nli-shadow [INPUT=…]` / `make export-nli-onnx` / the candidate
+bench all run on the GPU with no further wiring. The pattern: ship the
+laptop's `bench-qa` JSONL to the GPU box, run the sweep there in
+seconds. (`ai` doesn't have the ~6 GB shard corpus, so `make bench-qa`
+itself still runs on the laptop — that's the Hermes LLM, not local
+GPU; the GPU only accelerates the NLI half.)
 
 *The gate-item-4 number, at proper n.* `ARBORIST_NLI_SHADOW=1 make
 bench-qa BENCH_QA_N=1` → 75 q × 3 modes, of which 223 cells completed