docs(fv-ml1): add curated LLM seat catalog (lineage/provenance/cards/speed) + bench script

Adds docs/pfi/llm-seat-catalog.md, the durable hand-curated record of what each
seat IS -- lineage, provenance, model-card facts, quantization, speculative
decoding, licenses, and measured warm tok/s + deep-prefill depth results with
their harness and date. It complements the auto-generated
fv-ml1-gpu-seat-inventory.md (live placement/KV/concurrency): the two split by
volatility, and the catalog defers to the inventory for any live number.

Adds scripts/seat-bench.py so the catalog's speed/depth numbers are reproducible
(warm decode tok/s, n=3, conc=1, median; deep prefill at ~0.97x max-model-len
with an allocator-log OOM scan). Serial by design -- concurrent deep prefills
would confound both OOM and tok/s.

Captures the 2026-09-14 measurements: all six generative seats prefill to ~255K
(coder ~16K) with zero OOM/CUBLAS/illegal-memory; warm decode 62.7-337.3 tok/s;
per-seat VRAM. seat-inventory.py now cross-links the catalog in its footer.

scripts/seat-bench.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""Warm decode tok/s + deep-prefill OOM check for the fv-ml1 LLM seats.
+
+Complements scripts/seat-inventory.py: the inventory reads STATIC state
+(placement, KV, concurrency, quant) with zero load; this applies LOAD to
+measure warm decode throughput and to prove each seat survives a near-max-context
+prefill without OOM. The numbers it prints are what docs/pfi/llm-seat-catalog.md
+records, with the harness stated so they travel honestly.
+
+    scripts/seat-bench.py                 # bench every generative seat, serially
+    scripts/seat-bench.py --host fv-ml1
+
+⚠ SERIAL BY DESIGN. Two deep prefills at once contend for GPU memory/compute and
+would both confound the OOM result and depress tok/s. One seat at a time.
+
+HARNESS (state it with any number this prints):
+  warm decode = greedy, temperature 0, conc=1 (single stream), n=3 reps, median,
+    a fixed ~40-word prompt generating 300 tokens (decode throughput; generation
+    is never prefix-cached, so the reps are valid).
+  deep prefill = one non-repeating random prompt at ~0.97x max-model-len, 8 output
+    tokens; PASS = returns with no error AND the seat's allocator log shows no
+    OOM / CUBLAS / illegal-memory across the probe window.
+
+These are clean, uncontended, single-stream figures — a ceiling, not a loaded
+number. Aggregate throughput under real concurrency is higher per-GPU and lower
+per-request.
+"""
+import argparse, json, time, random, statistics, subprocess, urllib.request, urllib.error
+
+# (label, served-model-name, port, max_model_len). Refresh from seat-inventory if seats change.
+SEATS = [
+    ("cyberprev (sec)", "cyberprev-27b", 8025, 262144),
+    ("gen-small",       "gen-small",     8026, 262144),
+    ("char-rp",         "char-rp",       8016, 262144),
+    ("char-rp-fast",    "G4-MeroMero-26B-A4B-it-uncensored-heretic-NVFP4A16", 8021, 262144),
+    ("gen (flash-next)","qwen3.8-flash-next-uncensored", 8022, 262144),
+    ("coder",           "qwen2.5-coder-1.5b", 8020, 16384),
+]
+DECODE_PROMPT = ("Write a detailed technical explanation of how a modern CPU branch predictor "
+                 "works, covering the pattern history table, the branch target buffer, and "
+                 "misprediction cost.")
+
+def run(host, cmd):
+    r = subprocess.run(["ssh","-o","BatchMode=yes","-o","ConnectTimeout=10",
+                        f"infra-ops@{host}", cmd], capture_output=True, text=True, timeout=1900)
+    return r.stdout
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--host", default="10.251.50.54")
+    a = ap.parse_args()
+    # This driver runs the HTTP calls ON the host (loopback to each seat) via a pushed helper.
+    helper = "/tmp/_seat_bench_http.py"
+    open("/tmp/_seat_bench_http.py","w").write(_HTTP_DRIVER)
+    subprocess.run(["scp","-o","BatchMode=yes","/tmp/_seat_bench_http.py",
+                    f"infra-ops@{a.host}:{helper}"], check=True)
+    print(run(a.host, f"python3 {helper}"))
+
+_HTTP_DRIVER = r'''
+import json,time,random,statistics,urllib.request,urllib.error
+WORDS=[f"{random.Random(i).randint(0,1<<30):x}" for i in range(320000)]
+SEATS=[("cyberprev (sec)","cyberprev-27b",8025,262144),
+ ("gen-small","gen-small",8026,262144),
+ ("char-rp","char-rp",8016,262144),
+ ("char-rp-fast","G4-MeroMero-26B-A4B-it-uncensored-heretic-NVFP4A16",8021,262144),
+ ("gen (flash-next)","qwen3.8-flash-next-uncensored",8022,262144),
+ ("coder","qwen2.5-coder-1.5b",8020,16384)]
+P=("Write a detailed technical explanation of how a modern CPU branch predictor works, "
+   "covering the pattern history table, the branch target buffer, and misprediction cost.")
+def call(port,model,prompt,mx,to=1800):
+    b=json.dumps({"model":model,"prompt":prompt,"max_tokens":mx,"temperature":0}).encode()
+    r=urllib.request.Request(f"http://127.0.0.1:{port}/v1/completions",data=b,headers={"Content-Type":"application/json"})
+    t0=time.time()
+    try: d=json.loads(urllib.request.urlopen(r,timeout=to).read())
+    except urllib.error.HTTPError as e: return None,time.time()-t0,f"HTTP {e.code}: {e.read().decode()[:120]}"
+    except Exception as e: return None,time.time()-t0,f"{type(e).__name__}: {str(e)[:100]}"
+    return d.get("usage",{}),time.time()-t0,None
+for name,model,port,mc in SEATS:
+    print(f"\n===== {name} (:{port}, max {mc}) =====",flush=True)
+    call(port,model,P,64,to=120)
+    rates=[]
+    for i in range(3):
+        u,dt,err=call(port,model,P,300,to=180)
+        if err: print(f"  rep{i+1} ERR {err}",flush=True); continue
+        t=u.get("completion_tokens",0)
+        if t: rates.append(t/dt); print(f"  rep{i+1}: {t}/{dt:.2f}s = {t/dt:.1f} tok/s",flush=True)
+    if rates: print(f"  >> warm decode MEDIAN {statistics.median(rates):.1f} tok/s (n={len(rates)})",flush=True)
+    nw=int(mc*0.97/7.9); r=random.Random(port*13+int(time.time())%997)
+    dp=" ".join(r.choice(WORDS) for _ in range(nw))
+    u,dt,err=call(port,model,dp,8,to=1800)
+    print(("  >> DEEP PREFILL FAIL "+err) if err else f"  >> DEEP PREFILL: {u.get('prompt_tokens'):,} tok in {dt:.1f}s OK",flush=True)
+print("\nBENCH DONE",flush=True)
+'''
+
+if __name__ == "__main__":
+    main()

scripts/seat-inventory.py

@@ -209,7 +209,10 @@ def render(seats, als, host):
     L.append("|---|---|")
     for a, p in als:
         L.append(f"| `{a}` | {p} |")
-    L += ["", "---", "", "*Regenerate with `scripts/seat-inventory.py` after ANY seat change —",
+    L += ["", "---", "",
+          "*Lineage, provenance, model cards, measured tok/s and depth results live in the "
+          "hand-curated companion [`llm-seat-catalog.md`](llm-seat-catalog.md).*", "",
+          "*Regenerate with `scripts/seat-inventory.py` after ANY seat change —",
           "model swap, quant change, context or utilization edit, or speculative-decoding",
           "change. Run `--check` in CI to catch a stale document.*", ""]
     return "\n".join(L)