tools: add llama.cpp comparison baseline + standard benchmark suite

Vendor llama.cpp as a submodule pinned to b9371 and add a one-click
benchmark driver that compares xserv against it on identical workloads:

- setup-llama-cpp.sh: network-optional CUDA build (SM120); convert-to-gguf.sh
  converts the same safetensors to BF16 GGUF for an apples-to-apples baseline.
- tools/bench/: black-box OpenAI-API driver measuring TTFT/TPOT/throughput
  (single-stream + concurrent) and response quality on AIME 2025 + GSM8K.
- fetch_datasets.py pulls datasets to local JSON (GPU host has no network);
  task loaders prefer the local JSON.
- sync-and-build.sh: `bench` subcommand transfers source + datasets to the
  GPU host via tar-over-ssh (no rsync there), builds, and runs the suite.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tools/bench/tasks/__init__.py

@@ -0,0 +1,46 @@
+"""Shared helpers for quality tasks.
+
+Each task can be backed by a pre-fetched local JSON file (so the GPU host
+doesn't need network). The JSON is a list of records:
+    [{"id": str, "problem": str, "answer": str, "source": str}, ...]
+
+Use tools/bench/fetch_datasets.py on a networked machine to produce these
+files, then ship them to the GPU host (the bench sync does this automatically).
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Any
+
+
+def data_dir() -> str:
+    """Directory holding pre-fetched dataset JSON. Override via BENCH_DATA_DIR."""
+    return os.environ.get(
+        "BENCH_DATA_DIR",
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data"),
+    )
+
+
+def local_json_path(task_name: str) -> str:
+    return os.path.normpath(os.path.join(data_dir(), f"{task_name}.json"))
+
+
+def load_local(task_name: str) -> list[dict[str, Any]] | None:
+    """Return records from the local JSON file if present, else None."""
+    path = local_json_path(task_name)
+    if not os.path.isfile(path):
+        return None
+    with open(path) as f:
+        records = json.load(f)
+    print(f"[tasks] loaded {len(records)} records from {path}")
+    return records
+
+
+def save_local(task_name: str, records: list[dict[str, Any]]) -> str:
+    path = local_json_path(task_name)
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    with open(path, "w") as f:
+        json.dump(records, f, ensure_ascii=False, indent=1)
+    return path