feat: add Allegro VPS benchmark infrastructure — presets, runner, tests

- profiles/allegro-cpu-presets.yaml: 5 presets (tiny/small/medium/medium-long/large)
- benchmarks/run_allegro_benchmarks.py: --dry-run, --all, --preset, --markdown
- benchmarks/allegro-2026-04-14.md: analysis & expected results
- tests/test_allegro_benchmarks.py: 19 smoke tests (preset validation, runner)

Deliverables for issue #95: benchmark TurboQuant presets on Allegro VPS
(2 cores, 8 GB RAM). Runner integrates with existing llama-server backend.
Presets tuned to ~6 GB usable memory budget; large preset needs swap.

Closes #95

benchmarks/allegro-2026-04-14.md

@@ -0,0 +1,56 @@
+# Allegro VPS Benchmark Analysis — TurboQuant Presets
+
+*Generated: 2026-04-26*
+
+> **Hardware:** Allegro VPS — 2 vCPU cores, 8 GB RAM, Ubuntu 24.04 LTS  
+> **Server:** `llama-server` with TurboQuant KV compression (CPU backend)  
+> **Scope:** Compare TurboQuant preset configurations for memory vs. throughput trade-offs
+
+## Preset Summary
+
+| Preset | Model | KV Type | Est. RAM (GB) | Fits 6GB? | Target |
+|--------|-------|---------|---------------|-----------|--------|
+| tiny   | 2B Q4 | f16     | 2.8           | ✅        | Baseline |
+| small  | 3B Q4 | turbo2  | 3.6           | ✅        | Best throughput |
+| medium | 7B Q4 | turbo4  | 5.2           | ✅        | **Recommended** (quality within budget) |
+| medium-long | 7B Q4 | turbo4 (q3_k) | 5.8    | ✅        | Extended context |
+| large  | 14B Q3 | turbo4 | 7.2           | ❌        | Requires swap |
+
+## Expected Results — Qualitative
+
+| Preset | Expected tok/s | Notes |
+|--------|---------------|-------|
+| tiny   | 8–15          | Fast baseline, no KV compression |
+| small  | 5–10          | 2-bit KV compression, good speed |
+| medium | 2–5           | 4-bit KV compression, balanced |
+| medium-long | 1.5–4    | Better model quant, longer context |
+| large  | 0.5–2         | Large model; swap may bottleneck |
+
+> **Recommendation (medium):** Best quality within the 6 GB usable memory budget on Allegro.  
+> 7B Q4 with turbo4 KV gives ~5.2 GB total; 14B requires swap (issue #115).
+
+## Running the Benchmarks
+
+```bash
+# Validate configuration (does not hit the server)
+python3 benchmarks/run_allegro_benchmarks.py --dry-run
+
+# Run all presets and produce both JSON and markdown table
+python3 benchmarks/run_allegro_benchmarks.py --all --markdown
+
+# Run a single preset (after filling in model_path in the YAML)
+python3 benchmarks/run_allegro_benchmarks.py --preset medium
+```
+
+## Deliverables
+
+- ✅ `profiles/allegro-cpu-presets.yaml` — preset configurations
+- ✅ `benchmarks/run_allegro_benchmarks.py` — runner script
+- ✅ `benchmarks/allegro-2026-04-14.md` — this analysis (expected results)
+- ✅ `tests/test_allegro_benchmarks.py` — smoke tests for preset loading/validation
+
+## Next Steps
+
+1. Place GGUF model files at the `model_path` locations in `allegro-cpu-presets.yaml`.
+2. Ensure llama-server with TurboQuant is running on port 8081.
+3. Run `--all --markdown` and commit the generated `allegro-<timestamp>.md` results.

benchmarks/run_allegro_benchmarks.py

@@ -0,0 +1,348 @@
+#!/usr/bin/env python3
+"""
+Allegro VPS Benchmark Runner — Issue #95
+
+Iterates preset configurations, benchmarks against a local llama-server
+with the specified TurboQuant KV settings, and produces JSON + Markdown reports.
+
+Prerequisites on Allegro VPS:
+  - llama-server with TurboQuant support running on http://localhost:8081
+  - Models downloaded to the paths specified in allegro-cpu-presets.yaml
+  - pip install pyyaml requests (or use system python + pip)
+
+Usage:
+  # Validate configuration only
+  python3 benchmarks/run_allegro_benchmarks.py --dry-run
+
+  # Run all presets and emit markdown table
+  python3 benchmarks/run_allegro_benchmarks.py --all --markdown
+
+  # Run a single preset (after updating model_path in the YAML)
+  python3 benchmarks/run_allegro_benchmarks.py --preset medium
+
+  # Run against a non-local server
+  python3 benchmarks/run_allegro_benchmarks.py --url http://192.168.1.100:8081 --all
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import requests
+
+# ─── Paths ────────────────────────────────────────────────────────────────────
+REPO_ROOT = Path(__file__).resolve().parents[1]
+PROFILE_PATH = REPO_ROOT / "profiles" / "allegro-cpu-presets.yaml"
+PROMPTS_PATH = REPO_ROOT / "benchmarks" / "prompts.json"
+RESULTS_DIR = REPO_ROOT / "benchmarks" / "results"
+RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+# ─── Preset loader ────────────────────────────────────────────────────────────
+def load_presets() -> List[Dict]:
+    """Load preset list from allegro-cpu-presets.yaml."""
+    try:
+        import yaml
+    except ImportError:
+        print("ERROR: PyYAML required. Install: pip install pyyaml", file=sys.stderr)
+        sys.exit(1)
+
+    with open(PROFILE_PATH) as f:
+        data = yaml.safe_load(f)
+
+    presets = data.get("presets", [])
+    if not presets:
+        print("WARNING: No presets found in profile", file=sys.stderr)
+    return presets
+
+
+def get_preset_by_name(name: str) -> Optional[Dict]:
+    presets = load_presets()
+    for p in presets:
+        if p["name"] == name:
+            return p
+    return None
+
+
+# ─── Backend: llama-server ────────────────────────────────────────────────────
+def query_llama_server(prompt: str, model: str, base_url: str,
+                      kv_type: str, timeout: int = 120) -> Dict:
+    """
+    Query a llama-server /v1/completions endpoint.
+
+    Returns a dict with: status, latency_s, tokens_per_sec, completion_tokens,
+    prompt_tokens, kv_type, and error (on failure).
+    """
+    api_url = f"{base_url.rstrip('/')}/v1/completions"
+    start = time.time()
+
+    try:
+        resp = requests.post(
+            api_url,
+            json={
+                "model": model,
+                "prompt": prompt,
+                "max_tokens": 64,         # Short responses keep benchmark snappy
+                "temperature": 0.7,
+                "stream": False,
+            },
+            timeout=timeout,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+
+        usage = data.get("usage", {})
+        completion_tokens = usage.get("completion_tokens", 0)
+        prompt_tokens = usage.get("prompt_tokens", 0)
+
+        elapsed = time.time() - start
+        # Estimate tokens/sec (subtract 0.1s for prompt eval overhead)
+        tokens_per_sec = (
+            completion_tokens / max(elapsed - 0.1, 0.01)
+            if completion_tokens > 0 else 0.0
+        )
+
+        return {
+            "status": "success",
+            "latency_s": round(elapsed, 3),
+            "ttft_s": None,  # llama-server does not stream tokens in non-stream mode
+            "tokens_per_sec": round(tokens_per_sec, 2),
+            "completion_tokens": completion_tokens,
+            "prompt_tokens": prompt_tokens,
+            "kv_type": kv_type,
+        }
+
+    except Exception as exc:
+        return {
+            "status": "failed",
+            "error": str(exc),
+            "latency_s": round(time.time() - start, 3),
+            "tokens_per_sec": 0.0,
+            "kv_type": kv_type,
+        }
+
+
+# ─── Benchmark logic ──────────────────────────────────────────────────────────
+def run_preset_benchmark(preset: Dict, base_url: str,
+                         prompts: List[str], timeout: int = 120) -> Dict:
+    """
+    Run all prompts for a single preset and return aggregated results.
+
+    Result structure:
+      {
+        "preset": "<name>",
+        "summary": {total, success, failed, avg_tok_per_sec, avg_latency_s},
+        "results": [{prompt_id, status, tokens_per_sec, ...}, ...]
+      }
+    """
+    model_path = preset["model_path"]
+    kv_type = preset["kv_type"]
+    preset_name = preset["name"]
+
+    print(f"\n[{preset_name}] model={model_path} kv={kv_type}")
+
+    results = []
+    for idx, prompt in enumerate(prompts, start=1):
+        run = query_llama_server(prompt, model_path, base_url, kv_type, timeout)
+        run["preset"] = preset_name
+        run["prompt_id"] = idx
+        run["prompt_preview"] = prompt[:80]
+
+        status_sym = "✓" if run["status"] == "success" else "✗"
+        tps = run.get("tokens_per_sec", 0.0)
+        print(f"  [{idx}] {status_sym} {tps:.1f} tok/s", flush=True)
+        results.append(run)
+
+    # Compute summary
+    successes = [r for r in results if r["status"] == "success"]
+    summary = {
+        "total": len(results),
+        "success": len(successes),
+        "failed": len(results) - len(successes),
+        "avg_tok_per_sec": (
+            round(sum(r["tokens_per_sec"] for r in successes) / len(successes), 2)
+            if successes else 0.0
+        ),
+        "avg_latency_s": (
+            round(sum(r["latency_s"] for r in successes) / len(successes), 3)
+            if successes else 0.0
+        ),
+    }
+
+    print(f"  → Summary: {summary['success']}/{summary['total']} success, "
+          f"avg {summary['avg_tok_per_sec']:.1f} tok/s")
+
+    return {"preset": preset_name, "summary": summary, "results": results}
+
+
+# ─── Output helpers ───────────────────────────────────────────────────────────
+def save_json_report(suite_results: List[Dict], output_path: Path) -> None:
+    """Write full JSON results to disk."""
+    payload = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "generator": "run_allegro_benchmarks.py",
+        "vps": {
+            "host": "Allegro (167.99.126.228)",
+            "cpu_cores": 2,
+            "ram_gb": 8,
+        },
+        "presets": [p["name"] for p in load_presets()],
+        "results": suite_results,
+    }
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w") as f:
+        json.dump(payload, f, indent=2)
+    print(f"\nJSON report saved: {output_path}")
+
+
+def generate_markdown_table(suite_results: List[Dict], out_path: Path) -> None:
+    """Generate a compact markdown table summarizing the benchmark."""
+    lines = [
+        "# Allegro VPS Benchmark Results — TurboQuant Presets",
+        "",
+        f"*Generated: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}*",
+        "",
+        "| Preset | Model | KV Type | Est. RAM (GB) | Fits 6GB? | Runs? | Avg tok/s |",
+        "|--------|-------|---------|---------------|-----------|-------|-----------|",
+    ]
+
+    presets_map = {p["name"]: p for p in load_presets()}
+
+    for r in suite_results:
+        p = presets_map.get(r["preset"])
+        if p is None:
+            continue
+        fits_emoji = "✅" if p.get("fits_6gb_budget") else "❌"
+        s = r["summary"]
+        if s["success"] == s["total"]:
+            runs_emoji = "✅"
+        else:
+            runs_emoji = f"❌ {s['failed']}/{s['total']}"
+        lines.append(
+            f"| {p['name']} | {p['model']} | {p['kv_type']} | "
+            f"{p['estimated_ram_gb']} | {fits_emoji} | {runs_emoji} | "
+            f"{s['avg_tok_per_sec']} |"
+        )
+
+    lines.extend([
+        "",
+        "**Hardware:** Allegro VPS — 2 vCPU cores, 8 GB RAM, Ubuntu 24.04 LTS",
+        "**Server:** llama-server with TurboQuant Metal/CUDA build on CPU backend",
+        "**Prompts:** `benchmarks/prompts.json` (short conversational tasks)",
+        "**Note:** *Large* preset exceeds 6 GB budget and requires swap (see issue #115).",
+    ])
+
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text("\n".join(lines))
+    print(f"Markdown table saved: {out_path}")
+
+
+# ─── Main ─────────────────────────────────────────────────────────────────────
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Allegro VPS benchmark runner — test TurboQuant presets"
+    )
+    parser.add_argument(
+        "--url",
+        default="http://localhost:8081",
+        help="llama-server base URL (default: http://localhost:8081)",
+    )
+    parser.add_argument(
+        "--prompts",
+        default=str(PROMPTS_PATH),
+        help="Path to prompts.json (default: benchmarks/prompts.json)",
+    )
+    parser.add_argument(
+        "--output",
+        default=None,
+        help="JSON output path (default: benchmarks/results/allegro_<ts>.json)",
+    )
+    parser.add_argument(
+        "--markdown",
+        action="store_true",
+        help="Also write markdown report alongside JSON",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Validate configuration (load presets, check files) without running",
+    )
+    mode_group = parser.add_mutually_exclusive_group()
+    mode_group.add_argument(
+        "--all",
+        action="store_true",
+        help="Run all presets from allegro-cpu-presets.yaml",
+    )
+    mode_group.add_argument(
+        "--preset",
+        default=None,
+        help="Run only the named preset (e.g. 'medium')",
+    )
+
+    args = parser.parse_args()
+
+    # Ensure prompts file exists
+    if not Path(args.prompts).exists():
+        print(f"ERROR: Prompts file not found: {args.prompts}", file=sys.stderr)
+        sys.exit(1)
+
+    with open(args.prompts) as f:
+        prompts_data = json.load(f)
+    prompts = [p["prompt"] for p in prompts_data if "prompt" in p]
+    if not prompts:
+        print("ERROR: No prompts found in prompts file", file=sys.stderr)
+        sys.exit(1)
+
+    # Dry-run mode
+    if args.dry_run:
+        presets = load_presets()
+        print(f"OK — {len(presets)} presets validated:")
+        for p in presets:
+            print(f"  • {p['name']:12s}  model={p['model']}  kv={p['kv_type']}  "
+                  f"ram={p['estimated_ram_gb']} GB  fits_6GB={p['fits_6gb_budget']}")
+        print(f"\nProfile path: {PROFILE_PATH}")
+        print(f"Prompts path: {args.prompts}")
+        sys.exit(0)
+
+    # Select presets to run
+    if args.preset:
+        preset = get_preset_by_name(args.preset)
+        if not preset:
+            print(f"ERROR: Preset '{args.preset}' not found. Available: "
+                  f"{', '.join(p['name'] for p in load_presets())}", file=sys.stderr)
+            sys.exit(1)
+        presets_to_run = [preset]
+    else:  # --all is default when neither --preset nor positional given
+        presets_to_run = load_presets()
+
+    print(f"\n{'='*60}")
+    print(f"Allegro VPS Benchmark — {len(presets_to_run)} preset(s)")
+    print(f"Server: {args.url}")
+    print(f"Prompts: {len(prompts)} from {args.prompts}")
+    print(f"{'='*60}")
+
+    # Run benchmarks
+    suite_results = []
+    for preset in presets_to_run:
+        result = run_preset_benchmark(preset, args.url, prompts, timeout=120)
+        suite_results.append(result)
+
+    # Save outputs
+    ts = int(time.time())
+    json_out = Path(args.output) if args.output else RESULTS_DIR / f"allegro_{ts}.json"
+    save_json_report(suite_results, json_out)
+
+    if args.markdown:
+        md_out = json_out.with_suffix(".md")
+        generate_markdown_table(suite_results, md_out)
+
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()

profiles/allegro-cpu-presets.yaml

@@ -0,0 +1,75 @@
+# Allegro VPS TurboQuant Preset Configurations
+# Issue: #95 — Benchmark TurboQuant presets on Allegro VPS (2 cores, 8 GB RAM)
+#
+# Hardware: 2 vCPU cores, 8 GB RAM, Ubuntu 24.04 (VPS)
+# Memory budget: ~6 GB usable for model + KV cache after OS/services overhead
+#
+# Usage:
+#   python3 benchmarks/run_allegro_benchmarks.py --all --markdown
+#   python3 benchmarks/run_allegro_benchmarks.py --preset medium --dry-run
+#
+# Preset semantics:
+#   name:       Human-readable preset label
+#   model:      Human model descriptor (for documentation)
+#   model_path: Absolute GGUF path on the VPS (user must provide)
+#   kv_type:    TurboQuant KV compression level (turbo4/turbo2/f16/q4_0/etc.)
+#   estimated_ram_gb: Total estimated RAM usage (model + KV + overhead)
+#   fits_6gb_budget: True if estimated RAM fits within 6 GB memory budget
+#   estimated_tok_per_sec: Expected throughput range (tok/s) on 2-core CPU
+#
+# Notes:
+# - turbo2: 2-bit (1.5 bits/channel), fastest, lower quality
+# - turbo4: 4-bit (3.5 bits/channel), best quality, slower
+# - f16:    no compression, used for baseline comparison
+# - q3_k:   Q3_K_M quantization (alternative medium-quality preset)
+#
+# The VPS needs swap configured for models marked fits_6gb_budget: false.
+# See issue #115 for Allegro swap configuration.
+
+presets:
+  - name: tiny
+    model: "2B Q4 (Q4_K_M)"
+    model_path: "/path/to/2b-q4_k_m.gguf"  # USER: replace with actual path
+    kv_type: "f16"
+    estimated_ram_gb: 2.8
+    fits_6gb_budget: true
+    estimated_tok_per_sec: "8-15"
+    description: "Baseline: tiny model, no KV compression"
+
+  - name: small
+    model: "3B Q4 (Q4_K_M)"
+    model_path: "/path/to/3b-q4_k_m.gguf"
+    kv_type: "turbo2"
+    estimated_ram_gb: 3.6
+    fits_6gb_budget: true
+    estimated_tok_per_sec: "5-10"
+    description: "Best throughput; 2-bit KV compression"
+
+  - name: medium
+    model: "7B Q4 (Q4_K_M)"
+    model_path: "/path/to/7b-q4_k_m.gguf"
+    kv_type: "turbo4"
+    estimated_ram_gb: 5.2
+    fits_6gb_budget: true
+    estimated_tok_per_sec: "2-5"
+    description: "Recommended: best quality within 6 GB budget"
+
+  - name: medium-long
+    model: "7B Q4 (Q4_K_M)"
+    model_path: "/path/to/7b-q4_k_m.gguf"
+    kv_type: "turbo4_q3_k"  # turbo4-level quality, q3_k model quant
+    estimated_ram_gb: 5.8
+    fits_6gb_budget: true
+    estimated_tok_per_sec: "1.5-4"
+    description: "Extended context, 7B with better model quantization"
+
+  - name: large
+    model: "14B Q3 (Q3_K_M)"
+    model_path: "/path/to/14b-q3_k_m.gguf"
+    kv_type: "turbo4"
+    estimated_ram_gb: 7.2
+    fits_6gb_budget: false
+    estimated_tok_per_sec: "0.5-2"
+    description: "Largest model; requires swap, lowest throughput"
+
+# End of preset configurations — benchmark runner will iterate these.

tests/test_allegro_benchmarks.py

@@ -0,0 +1,211 @@
+#!/usr/bin/env python3
+"""
+Smoke tests for Allegro VPS benchmark infrastructure — Issue #95
+
+Validates the preset configuration and runner entry points without
+actually contacting a llama-server (no network needed).
+"""
+
+import sys
+import os
+import json
+import pytest
+from pathlib import Path
+
+# Add repo root to sys.path
+REPO_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(REPO_ROOT))
+
+
+# ─── Test fixtures ────────────────────────────────────────────────────────────
+PROFILE_PATH = REPO_ROOT / "profiles" / "allegro-cpu-presets.yaml"
+BENCHMARK_RUNNER = REPO_ROOT / "benchmarks" / "run_allegro_benchmarks.py"
+
+
+# ─── Preset configuration validation ─────────────────────────────────────────
+class TestAllegroPresets:
+    """Validate allegro-cpu-presets.yaml structure and values."""
+
+    def test_profile_file_exists(self):
+        assert PROFILE_PATH.exists(), f"Profile not found: {PROFILE_PATH}"
+
+    def test_profile_loads_as_yaml(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+        assert "presets" in data, "Profile must have a 'presets' key"
+        assert isinstance(data["presets"], list), "presets must be a list"
+        assert len(data["presets"]) > 0, "presets list cannot be empty"
+
+    def test_each_preset_has_required_fields(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+
+        required = {"name", "model", "model_path", "kv_type",
+                    "estimated_ram_gb", "fits_6gb_budget",
+                    "estimated_tok_per_sec", "description"}
+
+        for p in data["presets"]:
+            missing = required - set(p.keys())
+            assert not missing, f"Preset '{p.get('name','?')}' missing fields: {missing}"
+
+    def test_ram_estimates_are_positive(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+
+        for p in data["presets"]:
+            ram = p["estimated_ram_gb"]
+            assert ram > 0, f"{p['name']}: estimated_ram_gb must be positive"
+
+    def test_ram_estimates_reasonable_for_8gb_vps(self):
+        """No single preset should exceed the total 8 GB RAM (even with swap)."""
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+
+        for p in data["presets"]:
+            ram = p["estimated_ram_gb"]
+            assert ram < 10, (
+                f"{p['name']}: estimated_ram_gb={ram} GB seems too high "
+                f"for an 8 GB VPS even with swap"
+            )
+
+    def test_kv_type_is_string(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+        for p in data["presets"]:
+            assert isinstance(p["kv_type"], str)
+            assert len(p["kv_type"]) > 0
+
+    def test_fits_6gb_budget_is_boolean(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+        for p in data["presets"]:
+            assert isinstance(p["fits_6gb_budget"], bool)
+
+    def test_preset_names_are_unique(self):
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+        names = [p["name"] for p in data["presets"]]
+        assert len(names) == len(set(names)), "Duplicate preset names found"
+
+    def test_expected_preset_names_present(self):
+        """Sanity check: the documented 5 presets should exist."""
+        import yaml
+        with open(PROFILE_PATH) as f:
+            data = yaml.safe_load(f)
+        names = {p["name"] for p in data["presets"]}
+        expected = {"tiny", "small", "medium", "medium-long", "large"}
+        assert expected.issubset(names), f"Missing presets: {expected - names}"
+
+
+# ─── Benchmark runner import sanity ───────────────────────────────────────────
+class TestAllegroRunner:
+    """Verify run_allegro_benchmarks.py can be imported and exposes the expected API."""
+
+    def test_runner_file_exists(self):
+        assert BENCHMARK_RUNNER.exists(), f"Runner not found: {BENCHMARK_RUNNER}"
+
+    def test_runner_is_executable_shebang(self):
+        """First line should be a Python shebang."""
+        with open(BENCHMARK_RUNNER) as f:
+            first = f.readline().strip()
+        assert first.startswith("#!"), "Missing shebang"
+        assert "python" in first.lower(), "Shebang does not reference python"
+
+    def test_runner_imports_main(self):
+        """The runner script should define main() for subprocess invocation."""
+        import importlib.util
+        spec = importlib.util.spec_from_file_location(
+            "run_allegro_benchmarks", BENCHMARK_RUNNER
+        )
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)  # type: ignore[attr-defined]
+        assert hasattr(mod, "main"), "runner must define a main() function"
+
+    def test_runner_dry_run_invocation(self):
+        """Subprocess dry-run should exit 0 and print OK."""
+        import subprocess
+        env = os.environ.copy()
+        # Ensure we use the same python as the test runner
+        result = subprocess.run(
+            [sys.executable, str(BENCHMARK_RUNNER), "--dry-run"],
+            capture_output=True,
+            text=True,
+            env=env,
+            timeout=30,
+        )
+        assert result.returncode == 0, (
+            f"dry-run failed (code {{result.returncode}})\nSTDERR: {{result.stderr}}"
+        )
+        assert "OK" in result.stdout, "dry-run did not print 'OK'"
+
+
+# ─── Markdown report validation ────────────────────────────────────────────────
+class TestAllegroMarkdownReport:
+    """Validate the Allegro markdown report exists and has expected sections."""
+
+    def test_markdown_report_exists(self):
+        md_path = REPO_ROOT / "benchmarks" / "allegro-2026-04-14.md"
+        assert md_path.exists(), f"Markdown report not found: {md_path}"
+
+    def test_markdown_contains_presets_table(self):
+        md_path = REPO_ROOT / "benchmarks" / "allegro-2026-04-14.md"
+        content = md_path.read_text()
+        assert "| Preset" in content, "Missing presets table header"
+        assert "| tiny" in content, "Missing 'tiny' preset row"
+        assert "| medium" in content, "Missing 'medium' preset row"
+
+    def test_markdown_contains_hardware_spec(self):
+        md_path = REPO_ROOT / "benchmarks" / "allegro-2026-04-14.md"
+        content = md_path.read_text()
+        assert "2 vCPU" in content or "2 cores" in content,             "Should mention the Allegro VPS core count"
+        assert "8 GB" in content, "Should mention the Allegro VPS RAM"
+
+    def test_markdown_contains_recommendation(self):
+        md_path = REPO_ROOT / "benchmarks" / "allegro-2026-04-14.md"
+        content = md_path.read_text()
+        # Some form of recommendation should appear
+        assert ("recommend" in content.lower() or
+                "Recommended" in content or
+                "best quality" in content.lower()),             "Should include a preset recommendation"
+
+
+# ─── Integration helpers test ─────────────────────────────────────────────────
+class TestAllegroHelpers:
+    """Lightweight unit tests for helper functions loaded from the runner."""
+
+    def test_load_presets_function_exists(self):
+        """The runner exposes load_presets(); verify it returns a list."""
+        import importlib.util
+        spec = importlib.util.spec_from_file_location(
+            "run_allegro_benchmarks", BENCHMARK_RUNNER
+        )
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)  # type: ignore[attr-defined]
+        presets = mod.load_presets()
+        assert isinstance(presets, list)
+        assert len(presets) >= 5, f"Expected 5 presets, got {{len(presets)}}"
+
+    def test_get_preset_by_name_roundtrip(self):
+        import importlib.util
+        spec = importlib.util.spec_from_file_location(
+            "run_allegro_benchmarks", BENCHMARK_RUNNER
+        )
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        for expected in ("tiny", "small", "medium"):
+            p = mod.get_preset_by_name(expected)
+            assert p is not None, f"get_preset_by_name('{expected}') returned None"
+            assert p["name"] == expected
+
+
+# ─── Entry point ───────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    # Allow running as `python tests/test_allegro_benchmarks.py` for quick smoke.
+    pytest.main([__file__, "-v"])