refactor(phase-01): v3 retune fast & balanced roles

fast (Gemma 4 26B-A4B):
- Enable mmproj GPU loading (vision ~1s, 12x faster than CPU)
- KV f16 → q8_0 (save ~2.5 GB VRAM for mmproj)
- Tensor split 0.5,0.5 → 0.43,0.57 (13/17 layers)
- Remove --mlock/--poll/--prio/-t/-tb (no measurable impact)
- measured_tps 74.65 → 71.89 (trade 3.7% speed for vision)

balanced (Qwen 3.5 35B-A3B):
- Tensor split 0.5,0.5 → 0.48,0.52 (enables pipeline parallelism)
- Ubatch 128 → 256 (prefill +78%: 649 → 1,157 t/s)
- mmproj + --no-mmproj-offload (CPU vision, VRAM headroom)
- Remove useless flags same as fast
- measured_tps 61.62 → 64.16 (+4.1%)

Other:
- Document full retuning in docs/v3_{fast,balanced}_retuning_log.md
- Session report at .planning/reports/20260411-session-report.md
- Add bench utilities: bench_short/bench_long/test_ts_ratios
- Speculative decoding (E2B draft) experimented but rejected
  (+14% gen vs -31% cold start + tokenizer mismatch + mmproj conflict)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

scripts/bench_long.py

@@ -0,0 +1,67 @@
+"""Benchmark with long prompts to measure prompt processing (prefill) speed."""
+import json
+import time
+import urllib.request
+import sys
+
+try:
+    sys.stdout.reconfigure(encoding="utf-8")
+except Exception:
+    pass
+
+BASE_SENTENCE = (
+    "The history of computing is a vast and multifaceted journey that spans millennia, "
+    "from the earliest mechanical calculating aids to the sophisticated digital systems of today. "
+    "It begins with simple counting devices like the abacus, which originated in ancient Mesopotamia "
+    "around 2300 BCE and was later refined by Chinese and Roman civilizations. "
+    "These early tools laid the conceptual groundwork for mechanical computation. "
+)
+
+
+def make_prompt(seed):
+    # each seed produces a slightly different long prompt to defeat caching
+    unique = f"Session {seed}. Random seed value: {seed * 31337 + 17}. "
+    long_text = unique + (BASE_SENTENCE * 40)
+    return (
+        "Read the following text carefully, then answer in exactly one short sentence:\n\n"
+        f"{long_text}\n\n"
+        "Question: What is the main subject of the text above? Answer in one short sentence only."
+    )
+
+
+def bench(label, seed, gen_tokens=150):
+    payload = {
+        "model": "balanced",
+        "messages": [{"role": "user", "content": make_prompt(seed)}],
+        "max_tokens": gen_tokens,
+        "stream": False,
+        "temperature": 0.3,
+    }
+    req = urllib.request.Request(
+        "http://localhost:8000/v1/chat/completions",
+        data=json.dumps(payload).encode(),
+        headers={"Content-Type": "application/json"},
+    )
+    t0 = time.time()
+    with urllib.request.urlopen(req, timeout=600) as r:
+        d = json.loads(r.read())
+    total = time.time() - t0
+    t = d.get("timings", {})
+    print(f"[{label}]")
+    print(f"  prompt: {t['prompt_n']:>5} tok  {t['prompt_ms']:>7.0f} ms  {t['prompt_per_second']:>7.2f} t/s")
+    print(f"  gen:    {t['predicted_n']:>5} tok  {t['predicted_ms']:>7.0f} ms  {t['predicted_per_second']:>7.2f} t/s")
+    print(f"  total:  {total:.2f} s")
+    return t
+
+
+if __name__ == "__main__":
+    label = sys.argv[1] if len(sys.argv) > 1 else "run"
+    results = []
+    for i in range(3):
+        t = bench(f"{label} #{i+1}", seed=i + 1)
+        results.append(t)
+        print()
+    if results:
+        avg_prompt = sum(r["prompt_per_second"] for r in results) / len(results)
+        avg_gen = sum(r["predicted_per_second"] for r in results) / len(results)
+        print(f"=== [{label}] AVG === prompt: {avg_prompt:.2f} t/s | gen: {avg_gen:.2f} t/s")