---
type: reference
title: frank-small on Dankclaw — llama.cpp, Gemma4-12B QAT Q4_K_M
description: The active frank-small runbook — mainline llama.cpp on Dankclaw's RTX 3090 serving HauhauCS Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M with a separate MTP assistant sidecar chained behind ngram-mod, q8_0 KV, and --fit sizing the full 262k window: identity, payload, KV arithmetic, the spec-decode chain, deployed flags, the client contract, and what to check when it misbehaves.
tags:
  - llm
  - frank-small
  - dankclaw
  - llama.cpp
  - rtx3090
  - gemma
  - mtp
  - vision
timestamp: 2026-09-17
---

# frank-small — llama.cpp on the RTX 3090 (Gemma4-12B QAT)

> **ACTIVE since 2026-09-17.** Replaces [[frank-small-qwen38-gsq-rco]]
> (one-day rollback target). Same unit, same port, same client contract —
> the model, the drafter shape and the KV type changed.

## Identity

| what | value |
|---|---|
| runtime | mainline llama.cpp master `37b53fd`, CUDA build **with `-DGGML_CUDA_FA_ALL_QUANTS=ON`** (rebuilt 2026-09-17 for q8 KV) |
| binary | `/home/dank/GitPackages/llama.cpp-mainline/build-cuda/bin/llama-server` |
| unit | **root systemd unit** `frank-small.service` (repo `scripts/frank-small.service`, `relink_unit`ed into `/etc/systemd/system/`) |
| model | `HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced` Q4_K_M |
| model dir | `/home/dank/models/Gemma4-12B-QAT/` |
| port | 7771 (alias `frank-small`; `frank-large` keeps 7770) |
| window | **4 slots x 262,144** (`CTX_SIZE=1048576`, `PARALLEL=4`); q8_0 K and V |
| vision + audio | fleet image path via BF16 mmproj (`input: [text, image]`); `input_audio` transcribes verbatim (verified) |

## Payload (all sha256-verified against the Hub)

| file | bytes | sha256 (head…) |
|---|---|---|
| `Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf` | 7,381,381,760 | `59656d74…` |
| `mmproj-Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf` | 175,115,264 | `b59e8154…` |
| `mtp-gemma-4-12B-it.gguf` | 253,707,328 | `c50c91c3…` |

`sha256.txt` beside them on the host carries the full digests.

## What the preflight found (GGUF headers, read over HTTP Range)

- **Target** (`arch gemma4`, 48 blocks): ctx 262144, MHA — 16 kv heads x
  512 dim (no GQA), embedding 3840, rope base 1e6, logit softcap 30,
  **sliding_window 1024**, `swa_type = STANDARD`, per-layer token embeddings
  (PLE) via `LLM_KV_EMBEDDING_LENGTH_PER_LAYER`. 12 full-attention layers
  every 4th (`[3,7,…,47]` implied by `full_attention_interval = 4`).
  48 blocks maps to `LLM_TYPE_UNKNOWN` in the loader's size switch —
  cosmetic only.
- **mmproj**: clip, vision 224/16 patches, **also carries an audio
  encoder** (`has_audio_encoder=True`) — vision is the fleet contract,
  and audio works: a JFK WAV sent as an `input_audio` content part was
  transcribed verbatim (2026-09-17).
- **MTP sidecar** (`arch gemma4-assistant`): 4 nextn layers, pure
  assistant (no trunk). Its KV **shares** the target's last layers — the
  share lambda at `llama-model.cpp:2662` references
  `llama_model_n_layer(model_other) - 2` for SWA layers. Adding the
  drafter therefore costs no second KV pool.

## KV arithmetic — why four full windows fit

Per token per layer at q8_0: 16 heads x 512 dim x 1 B x 2 (K,V) =
**16 KiB** (32 KiB at f16). With SWA_STANDARD and window 1024, the
sliding layers cache only the window; the full-attention layers cache the
full context — the pattern that makes the window cheap (the naive
"all-global" worst case was 32 GiB at q8/262k, impossible).

Measured on the 3090 (24,576 MiB): one full-context slot idles at
11,996 MiB, four at 22,346 MiB — 3,450 MiB per slot, ~8,546 MiB fixed.
A fifth slot projects past the card, so **4 x 262,144 is the ceiling**.
`CTX_SIZE` is set explicitly to `PARALLEL x 262144` rather than left for
`--fit`: fit maximises the TOTAL pool and would divide a smaller window
per slot at `parallel > 1`. The slot-grid seam in
`tests/frank_small_unit.bats` pins both the full-window division and the
budget inequality so an oversized bump fails pre-deploy, not in boot.

## Spec-decode chain

`--spec-type draft-mtp,ngram-mod` with `--model-draft
$MODEL_DIR/mtp-gemma-4-12B-it.gguf`:

- `draft-mtp` is context-backed and reads the assistant sidecar. Two
  context-backed drafters cannot share one process, so the chain carries
  exactly one.
- `ngram-mod` is draftless and attaches unconditionally, catching
  prompt-echo traffic MTP would miss. It prints **no startup banner** —
  verify behaviorally via the per-task `draft acceptance` line, not the
  load log.
- Measured at deploy: `draft acceptance = 0.38–0.46, mean len ≈ 2.2`
  (lower acceptance than the retired Qwen in-file MTP's 0.66–0.94, but a
  much smaller model — the point of the swap was the VRAM and the window,
  not acceptance).

## Throughput tuning (measured 2026-09-17)

Salted API probes at the deployed geometry (4 slots, q8 KV, flash attn),
prefill ~254k tokens / decode at ~253k context:

| batch (b/ub) | draft n-max / poll | pp t/s | tg@250k t/s | idle MiB |
|---|---|---|---|---|
| 2048/512 (old) | 3 / 50 | 912–924 | 90*–204 | 22,346 |
| **8192/1024 (deployed)** | **3 / 50** | **962–965** | **226–302** | **23,300** |
| 8192/1024 | 4 / 100 | 961 | 230–257 | 23,300 |
| 4096/1024 | 4 / 100 | 959 | 205–277 | 23,300 |
| 8192/2048 | — | — | — | OOM |

- **ubatch 1024 is the winner**: +5% max-length prefill, and decode at
  250k clearly above the 512 baseline.
- **ubatch 2048 cannot run**: the MTP draft context needs ~3.3 GiB of pp
  compute buffer at that ubatch against ~2.2 GiB headroom — it dies in
  `common_speculative_init_result` after the main model loads.
- **Deeper draft and busy-wait HURT long-ctx decode**: draft n-max 4 with
  poll 100 verified a longer round per token, and at 250k the extra
  full-attention re-read outweighs the accepted-token gain (230–257 vs
  257–302). Keep 3 / 50.
- **All ubatch-1024 variants idle identically** (23,300 MiB) — compute
  buffers are reserved at max graph size regardless of the logical batch,
  so 8192 costs nothing over 4096.
- **Concurrency caveat**: a long prefill monopolises the GPU — a decode
  running beside a 254k-token prefill drops from ~240 to ~100–135 t/s.
  Four slots share one engine with no per-slot QoS; that is inherent, not
  a config fault.
- **No CUDA graphs in this build** — `--help` shows zero graph flags; the
  decode-launch-overhead lever does not exist here.

## Deployed flags

Repo truth: `scripts/frank-small.env` + `scripts/serve-frank-small.sh`.
Key values: `MODEL_DIR=/home/dank/models/Gemma4-12B-QAT`,
`MODEL_FILE=Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf`,
`DRAFT_FILE=mtp-gemma-4-12B-it.gguf`, `CACHE_TYPE_K=V=q8_0`,
`SPEC_TYPE=draft-mtp,ngram-mod`, `SPEC_DRAFT_N_MAX=3`, `POLL_LEVEL=50`,
`CTX_SIZE=1048576` with `PARALLEL=4` (four full 262,144-token slots),
`BATCH_SIZE=8192`, `UBATCH_SIZE=1024`, `NGL=999`, `FLASH_ATTN=on`,
`--jinja`, `--metrics`, greedy `--temp 0.0` server default. The serve
script refuses to start on any missing file.

## Client contract

- `reasoning_content` carries thinking; `preserve_thinking: true` is sent
  by the harness to keep it across turns.
- **Every `reasoning_effort` level is accepted** (all seven probed
  2026-09-17: 200) — unlike Qwen3.8's template gate. The harness's
  `reasoningEffortMap` in `~/.omp/agent/models.yml` is therefore inert
  but kept as a safety net.
- Tools work through the `--jinja` template (`tool_calls` verified).
- Images work through the mmproj (verified with a live JPEG).
- Four concurrent conversations (one per slot). No per-slot QoS: a long
  prefill in one slot slows decodes in the others — see the caveat above.
- Harness: `contextWindow: 262144`, `maxTokens: 32768`.

## Troubleshooting

- **`Gemma4Assistant requires ctx_other to be set` printed at boot** —
  the `--fit` probe, not a fault (fit cannot build the assistant context
  during measurement). With `CTX_SIZE` set explicitly, as deployed, this
  line does not appear at all. Do not restart over it either way.
- **Draft looks unengaged**: ngram-mod is silent at load; read the
  `draft acceptance = …` line per task from
  `journalctl -u frank-small` (the server must not be restarted just to
  see this).
- **Quantized KV regression**: if someone rebuilds `build-cuda` without
  `GGML_CUDA_FA_ALL_QUANTS=ON`, prefill collapses ~12x with q8 KV (see
  the CUDA lesson in AGENTS.md). Check
  `grep FA_ALL_QUANTS build-cuda/CMakeCache.txt` after any rebuild.
- Logs: `journalctl -u frank-small` (root unit, not quadlet).

## Rollback

Redeploying the prior model means repointing `MODEL_DIR`/`MODEL_FILE`/
`DRAFT_FILE` back to the Qwen trio and `SPEC_TYPE=draft-mtp` — see
[[frank-small-qwen38-gsq-rco]] for the exact deployed values. The Qwen
weights were NOT deleted from the host.

## References

- `scripts/serve-frank-small.sh` — the actual exec line
- [[frank-small-qwen38-gsq-rco]] — RETIRED predecessor (rollback)
- [[halogen-flash-server]] — the sibling endpoint on the iGPU
- [[x4-pair-experiment]] — why the 3090 serves its own model, not the trunk