Frank
b51e078e36
Verbosity 4 paid for itself: the engine's own llama_kv_cache lines give the exact accounting — the cache bills the K+V pair TWICE under attention_k_eq_v (6.125 B/elem at f16 K + q8_0 V), reproducing 3,001.25 MiB (5 full layers at n_head_kv 2) + 306.25 MiB (25 SWA rings at n_head_kv 8, window 1024) to the byte. The earlier "bill K-only" note was wrong; full layers have 2 KV heads, not 8. Deployed boot: 21,868 used / 2,257 free — the f16-K raise (+2,161 MiB KV) is funded by the ubatch 2048 -> 1024 cut (~616 MiB of compute buffer) plus variance, so exact K came essentially free. Measured decode 139.9 t/s temp 0; vision probe returns Red at prompt_tokens 151.
111 lines
5.5 KiB
Markdown
111 lines
5.5 KiB
Markdown
# frank-secondary — llama.cpp CUDA, Gemma4-26B-A4B Q4_K_M, 1×200,704 (Dankclaw)
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**ACTIVE since 2026-10-07.** RTX 3090, port 7771, root systemd unit
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`frank-secondary.service`, served by `scripts/serve-frank-secondary.sh` +
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`scripts/frank-secondary.env`. Supersedes
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[[frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688]] (Swift-1.5-Qwen3.8-27B
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RCO-IQ3_XXS-mtp, 2×114,688, in-file MTP).
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## Payload
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|File|Path|Bytes|
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|---|---|---|
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|Main (Q4_K_M)|`/home/dank/models/Gemma4-26B-A4B/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf`|16,796,015,520 on disk; the engine reports 16,780,192,888|
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|Vision (mmproj BF16)|`/home/dank/models/Gemma4-26B-A4B/mmproj-Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf`|~1.195 GiB|
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|MTP assistant (on disk, NOT loaded)|`/home/dank/models/Gemma4-26B-A4B/mtp-gemma-4-26B-A4B-it.gguf`|~0.252 GiB|
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Repo: `HauhauCS/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-MTP`
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(base `google/gemma-4-26B-A4B-it`). Downloaded 2026-10-07 by plain
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`curl -fL` (no token needed, ungated).
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## Shape
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```
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--ctx-size 200704 --parallel 1 # ONE conversation, Frank's 200k cap
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--flash-attn on
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--cache-type-k f16 --cache-type-v q8_0 # exact K, FP8-class V (q8_0)
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--batch-size 4096 --ubatch-size 1024
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--n-gpu-layers 99
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--log-verbosity 4 # debug: KV layout, buffers, graph capture
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--spec-type none # MTP OFF (see below)
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--mmproj <BF16 projector>
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--jinja --reasoning auto --reasoning-format deepseek
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```
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Measured boot at **q8_0 K + q8_0 V, ubatch 2048, MTP off** (2026-10-07
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morning): **21,890 MiB used / 2,235 MiB free** (`nvidia-smi`). With MTP ON
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(the assistant loaded) the same shape was **23,806 / 319** — the drafter's
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context costs ~1.9 GiB because its KV is built against the target's full
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window ([[Llama Cpp Serving Sizing]]).
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**Deployed shape (2026-10-07 evening, Frank): K = f16, ubatch 1024, verbosity
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4. Measured boot: 21,868 MiB used / 2,257 MiB free** — i.e. the f16-K raise
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(+2,161 MiB of KV, see the accounting below) was paid for by the ubatch cut
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(2048 → 1024 returns ~616 MiB of compute buffer) plus slot-geometry variance.
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Exact K came essentially free. The verbosity-4 ledger (journal, `llama_kv_cache`
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lines): model 16,002.91 MiB (CUDA0) + 577.50 MiB (CPU-mapped), **KV 3,001.25
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MiB (5 full layers) + 306.25 MiB (25 SWA rings)**, compute buffers 926.39 +
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150.63 MiB, output buffer 1.00 MiB.
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**Measured decode: 139.9 tok/s** (`/completion` timings, temp 0, 256 tokens,
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deployed shape) — the ~4 B-active MoE is at home on the 3090. The model
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thinks: a 256-token budget on a short prompt can be consumed entirely by
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`reasoning_content` with empty `content` and `finish_reason: length`, so
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harness requests must budget for thinking (same law as the uncensored Swift
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payload). Vision probe on this build: solid-red 512 px PNG via the OpenAI
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`image_url` part → `Red`, `prompt_tokens` 151 (the image reached the model).
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Decode is expected ubatch-invariant (Swift-era 3090 law; the 139.9 t/s above
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is at ubatch 1024 — one rung measured, not a ladder).
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## Architecture facts
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Gemma4 MoE hybrid: 30 layers, 128 experts (8 active), n_embd 2816, 262,144
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native window, n_vocab 262,144. Attention is hybrid per
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`gemma4.attention.sliding_window_pattern` (read from the file at load): **25
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sliding-window layers (window 1024, `n_head_kv = 8`) + 5 full-attention layers
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(indices 5, 11, 17, 23, 29, `n_head_kv = 2`)**, head_dim 256 for K and V in
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both classes. The KV pool is TWO caches: the full-context cache scales with
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the 5 layers at the full 200,704 window; the SWA cache is a fixed 1024-token
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ring per sliding layer.
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**The rate law (measured, exact):** bytes/token = `n_kv_heads × 256 × 6.125 B`
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per layer at f16 K + q8_0 V — `attention_k_eq_v` does **NOT** shrink the
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allocation: `Vcur = Kcur` is a compute-graph mapping, the cache reserves K and
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V slots and, with `k_eq_v`, reserves the pair **twice** (6.125 = 2 × (2 B f16 +
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1.0625 B q8_0)). Verified against the boot: 5 × 2 × 256 × 6.125 = 15,680
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B/token × 200,704 = **3,001.25 MiB** ✓ and 25 × 1024 × 8 × 256 × 6.125 =
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**306.25 MiB** ✓. At q8_0 K + q8_0 V the rate is 2.125 B/elem: the same shape
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costs 1,041 + 106 = **1,147 MiB**, so the f16-K raise is **+2,161 MiB**. The
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`--cache-type-v` flag is honoured, not inert.
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The llama.cpp master build at commit `5fc4f3c` supports the arch
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(`src/models/gemma4.cpp`, `LLM_ARCH_GEMMA4 = "gemma4"`). Note: `grep -c`
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arch names on the packed binary returns 0 for EVERY arch (including the
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`qwen3_5` it serves) — check the source tree, not the binary.
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## MTP law
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MTP for Gemma4 is a SEPARATE file (`mtp-gemma-4-26B-A4B-it.gguf`, arch
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`gemma4-assistant`), loaded via `--spec-draft-model` beside
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`--spec-type draft-mtp` — unlike Swift, whose MTP head ships inside the main
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GGUF. The serve script gained `SPEC_DRAFT_FILE` for this. 2026-10-07 (Frank):
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**MTP disabled** — `SPEC_TYPE=none` strips both spec flags (flags move as a
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set). The assistant payload stays on disk; setting `SPEC_TYPE=draft-mtp` +
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`SPEC_DRAFT_FILE` re-enables it at the cost of ~1.9 GiB (boots at 319 MiB
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free).
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## Verification
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- `curl http://192.168.0.143:7771/health` → `{"status":"ok"}`
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- `/v1/models` reports `n_ctx: 200704`, `ftype: Q4_K - Medium`
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- Vision probe: solid-color PNG ≥ 256 px via OpenAI `image_url` part,
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expect the exact color word.
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## Rollback
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Revert to the Swift trio: `MODEL_FILE=/home/dank/models/Swift15-UncIQ3XXS/…`,
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`CTX_SIZE=229376`, `PARALLEL=2`, in-file `SPEC_TYPE=draft-mtp` — the Swift
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payload remains on disk. See
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[[frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688]].
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