186 lines
9.0 KiB
Markdown
186 lines
9.0 KiB
Markdown
---
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type: reference
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title: frank-small on Dankclaw — llama.cpp, Gemma4-12B QAT Q4_K_M
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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.
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tags:
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- llm
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- frank-small
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- dankclaw
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- llama.cpp
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- rtx3090
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- gemma
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- mtp
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- vision
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timestamp: 2026-09-17
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---
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# frank-small — llama.cpp on the RTX 3090 (Gemma4-12B QAT)
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> **ACTIVE since 2026-09-17.** Replaces [[frank-small-qwen38-gsq-rco]]
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> (one-day rollback target). Same unit, same port, same client contract —
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> the model, the drafter shape and the KV type changed.
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## Identity
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| what | value |
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|---|---|
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| runtime | mainline llama.cpp master `37b53fd`, CUDA build **with `-DGGML_CUDA_FA_ALL_QUANTS=ON`** (rebuilt 2026-09-17 for q8 KV) |
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| binary | `/home/dank/GitPackages/llama.cpp-mainline/build-cuda/bin/llama-server` |
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| unit | **root systemd unit** `frank-small.service` (repo `scripts/frank-small.service`, `relink_unit`ed into `/etc/systemd/system/`) |
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| model | `HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced` Q4_K_M |
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| model dir | `/home/dank/models/Gemma4-12B-QAT/` |
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| port | 7771 (alias `frank-small`; `frank-large` keeps 7770) |
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| window | **4 slots x 262,144** (`CTX_SIZE=1048576`, `PARALLEL=4`); q8_0 K and V |
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| vision + audio | fleet image path via BF16 mmproj (`input: [text, image]`); `input_audio` transcribes verbatim (verified) |
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## Payload (all sha256-verified against the Hub)
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| file | bytes | sha256 (head…) |
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|---|---|---|
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| `Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf` | 7,381,381,760 | `59656d74…` |
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| `mmproj-Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf` | 175,115,264 | `b59e8154…` |
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| `mtp-gemma-4-12B-it.gguf` | 253,707,328 | `c50c91c3…` |
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`sha256.txt` beside them on the host carries the full digests.
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## What the preflight found (GGUF headers, read over HTTP Range)
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- **Target** (`arch gemma4`, 48 blocks): ctx 262144, MHA — 16 kv heads x
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512 dim (no GQA), embedding 3840, rope base 1e6, logit softcap 30,
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**sliding_window 1024**, `swa_type = STANDARD`, per-layer token embeddings
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(PLE) via `LLM_KV_EMBEDDING_LENGTH_PER_LAYER`. 12 full-attention layers
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every 4th (`[3,7,…,47]` implied by `full_attention_interval = 4`).
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48 blocks maps to `LLM_TYPE_UNKNOWN` in the loader's size switch —
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cosmetic only.
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- **mmproj**: clip, vision 224/16 patches, **also carries an audio
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encoder** (`has_audio_encoder=True`) — vision is the fleet contract,
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and audio works: a JFK WAV sent as an `input_audio` content part was
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transcribed verbatim (2026-09-17).
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- **MTP sidecar** (`arch gemma4-assistant`): 4 nextn layers, pure
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assistant (no trunk). Its KV **shares** the target's last layers — the
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share lambda at `llama-model.cpp:2662` references
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`llama_model_n_layer(model_other) - 2` for SWA layers. Adding the
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drafter therefore costs no second KV pool.
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## KV arithmetic — why four full windows fit
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Per token per layer at q8_0: 16 heads x 512 dim x 1 B x 2 (K,V) =
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**16 KiB** (32 KiB at f16). With SWA_STANDARD and window 1024, the
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sliding layers cache only the window; the full-attention layers cache the
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full context — the pattern that makes the window cheap (the naive
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"all-global" worst case was 32 GiB at q8/262k, impossible).
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Measured on the 3090 (24,576 MiB): one full-context slot idles at
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11,996 MiB, four at 22,346 MiB — 3,450 MiB per slot, ~8,546 MiB fixed.
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A fifth slot projects past the card, so **4 x 262,144 is the ceiling**.
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`CTX_SIZE` is set explicitly to `PARALLEL x 262144` rather than left for
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`--fit`: fit maximises the TOTAL pool and would divide a smaller window
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per slot at `parallel > 1`. The slot-grid seam in
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`tests/frank_small_unit.bats` pins both the full-window division and the
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budget inequality so an oversized bump fails pre-deploy, not in boot.
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## Spec-decode chain
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`--spec-type draft-mtp,ngram-mod` with `--model-draft
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$MODEL_DIR/mtp-gemma-4-12B-it.gguf`:
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- `draft-mtp` is context-backed and reads the assistant sidecar. Two
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context-backed drafters cannot share one process, so the chain carries
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exactly one.
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- `ngram-mod` is draftless and attaches unconditionally, catching
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prompt-echo traffic MTP would miss. It prints **no startup banner** —
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verify behaviorally via the per-task `draft acceptance` line, not the
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load log.
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- Measured at deploy: `draft acceptance = 0.38–0.46, mean len ≈ 2.2`
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(lower acceptance than the retired Qwen in-file MTP's 0.66–0.94, but a
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much smaller model — the point of the swap was the VRAM and the window,
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not acceptance).
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## Throughput tuning (measured 2026-09-17)
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Salted API probes at the deployed geometry (4 slots, q8 KV, flash attn),
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prefill ~254k tokens / decode at ~253k context:
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| batch (b/ub) | draft n-max / poll | pp t/s | tg@250k t/s | idle MiB |
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|---|---|---|---|---|
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| 2048/512 (old) | 3 / 50 | 912–924 | 90*–204 | 22,346 |
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| **8192/1024 (deployed)** | **3 / 50** | **962–965** | **226–302** | **23,300** |
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| 8192/1024 | 4 / 100 | 961 | 230–257 | 23,300 |
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| 4096/1024 | 4 / 100 | 959 | 205–277 | 23,300 |
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| 8192/2048 | — | — | — | OOM |
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- **ubatch 1024 is the winner**: +5% max-length prefill, and decode at
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250k clearly above the 512 baseline.
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- **ubatch 2048 cannot run**: the MTP draft context needs ~3.3 GiB of pp
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compute buffer at that ubatch against ~2.2 GiB headroom — it dies in
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`common_speculative_init_result` after the main model loads.
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- **Deeper draft and busy-wait HURT long-ctx decode**: draft n-max 4 with
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poll 100 verified a longer round per token, and at 250k the extra
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full-attention re-read outweighs the accepted-token gain (230–257 vs
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257–302). Keep 3 / 50.
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- **All ubatch-1024 variants idle identically** (23,300 MiB) — compute
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buffers are reserved at max graph size regardless of the logical batch,
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so 8192 costs nothing over 4096.
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- **Concurrency caveat**: a long prefill monopolises the GPU — a decode
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running beside a 254k-token prefill drops from ~240 to ~100–135 t/s.
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Four slots share one engine with no per-slot QoS; that is inherent, not
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a config fault.
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- **No CUDA graphs in this build** — `--help` shows zero graph flags; the
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decode-launch-overhead lever does not exist here.
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## Deployed flags
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Repo truth: `scripts/frank-small.env` + `scripts/serve-frank-small.sh`.
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Key values: `MODEL_DIR=/home/dank/models/Gemma4-12B-QAT`,
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`MODEL_FILE=Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf`,
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`DRAFT_FILE=mtp-gemma-4-12B-it.gguf`, `CACHE_TYPE_K=V=q8_0`, `CACHE_RAM=2048`,
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`SPEC_TYPE=draft-mtp,ngram-mod`, `SPEC_DRAFT_N_MAX=3`, `POLL_LEVEL=50`,
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`CTX_SIZE=1048576` with `PARALLEL=4` (four full 262,144-token slots),
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`BATCH_SIZE=8192`, `UBATCH_SIZE=1024`, `NGL=999`, `FLASH_ATTN=on`,
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`--jinja`, `--metrics`, greedy `--temp 0.0` server default. The serve
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script refuses to start on any missing file.
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## Client contract
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- `reasoning_content` carries thinking; `preserve_thinking: true` is sent
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by the harness to keep it across turns.
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- **Every `reasoning_effort` level is accepted** (all seven probed
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2026-09-17: 200) — unlike Qwen3.8's template gate. The harness's
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`reasoningEffortMap` in `~/.omp/agent/models.yml` is therefore inert
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but kept as a safety net.
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- Tools work through the `--jinja` template (`tool_calls` verified).
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- Images work through the mmproj (verified with a live JPEG).
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- Four concurrent conversations (one per slot). No per-slot QoS: a long
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prefill in one slot slows decodes in the others — see the caveat above.
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- Harness: `contextWindow: 262144`, `maxTokens: 32768`.
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## Troubleshooting
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- **`Gemma4Assistant requires ctx_other to be set` printed at boot** —
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the `--fit` probe, not a fault (fit cannot build the assistant context
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during measurement). With `CTX_SIZE` set explicitly, as deployed, this
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line does not appear at all. Do not restart over it either way.
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- **Draft looks unengaged**: ngram-mod is silent at load; read the
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`draft acceptance = …` line per task from
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`journalctl -u frank-small` (the server must not be restarted just to
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see this).
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- **Quantized KV regression**: if someone rebuilds `build-cuda` without
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`GGML_CUDA_FA_ALL_QUANTS=ON`, prefill collapses ~12x with q8 KV (see
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the CUDA lesson in AGENTS.md). Check
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`grep FA_ALL_QUANTS build-cuda/CMakeCache.txt` after any rebuild.
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- Logs: `journalctl -u frank-small` (root unit, not quadlet).
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## Rollback
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Redeploying the prior model means repointing `MODEL_DIR`/`MODEL_FILE`/
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`DRAFT_FILE` back to the Qwen trio and `SPEC_TYPE=draft-mtp` — see
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[[frank-small-qwen38-gsq-rco]] for the exact deployed values. The Qwen
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weights were NOT deleted from the host.
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## References
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- `scripts/serve-frank-small.sh` — the actual exec line
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- [[frank-small-qwen38-gsq-rco]] — RETIRED predecessor (rollback)
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- [[halogen-flash-server]] — the sibling endpoint on the iGPU
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- [[x4-pair-experiment]] — why the 3090 serves its own model, not the trunk
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