docs(frank-small): record the 3 x 180,224 + vision shape and what it cost

Runbook, both indexes, AGENTS.md (topology row, endpoint section, three new
Key Lessons, one stale SGLang label in the bench list) and the bundle log now
describe the deployed shape: pool 540,672, 180,224 tokens per conversation,
f16 K + q8_0 V, mmproj vision, 21,684 used / 2,441 free of 24,576 MiB, 106.8
tok/s decode, 3,814 t/s prefill.

The claims that mattered:
- three contexts buy 180,224 tokens per conversation, not the native 262,144,
  and a longer prompt is a hard 400 - so models.yml contextWindow is 180224
  and frank-primary's 183,501-token compaction request no longer fits here
  (forward pointer added to the retired SGLang runbook, which asserted it)
- the vision tower bills ~1.4 GiB for a 918 MB file, established by subtracting
  two measured boots, not by a formula
- /v1/chat/completions ignores the legacy top-level 'images' field: the model
  invents a color; only the image_url content part reaches it
This commit is contained in:
Frank
2026-10-02 03:46:50 -07:00
parent 7fe881de01
commit 934285c2b2
7 changed files with 245 additions and 67 deletions
+71 -25
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@@ -26,7 +26,7 @@ merge absorbed the former `vllmScripts` repo, history preserved).
|--------|---------|---------|-------|------|
| Dankserver | `frank-primary` | tcclaviger RDNA4 vLLM fork (`docker.io/tcclaviger/vllm@sha256:da4029666…`, digest-pinned, plain `podman run`, root systemd unit) | `orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8` (~172.8 GiB, 131 shards), TP=2, expert offload automatic (fork plans it), prefetch OFF, **bf16 KV** (`--kv-cache-dtype auto`), **no speculative decode**, MNBT 2048, MoE gather 32×64, **2 seqs × 262144 ctx (the model's full native window)** at KV_BYTES 7,300,000,000/rank (bf16 measures 13,839 B/token), with **`--expert-precise-memory`** — the fitted 5.25 GiB overhead instead of the 7.60 GiB default, which hands the compile/graph-capture headroom to the expert arena (~7,278 slots vs 6,248) — util 0.98, and the client contract wired: `--reasoning-parser qwen3` (thinking in `reasoning`, never inline), `--enable-auto-tool-choice --tool-call-parser qwen3_xml`, default chat-template kwargs `enable_thinking` + `preserve_thinking`. Capacity history: five full-length contexts boot but leave ~0 bytes free at runtime (a real request with `max_tokens=261149` killed the worker: `Tried to allocate 50.00 MiB ... 0 bytes free`); six never fit (6×241664 asked 10.70 GiB vs 9.18 GiB free). DCP cannot help here — it requires TP > the model's 2 KV heads, i.e. four GPUs. fp8+MTP4 shape restorable from git tag `frank-primary-fp8-mtp-20261001` | 7770 |
| Dankclaw | `frank-large` | **ROCmFPX llama.cpp fork** (`kingjones30/ROCmFPX` @ `36e9acd`, gfx1151, built by `scripts/build-rocmfpx.sh`; bare `llama-server`, root systemd unit) | `kingjones777/Qwen3.8-27B-ROCmFP4-STRIX-MTP-GGUF` **`Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf`** (26.28 GiB, ftype 115 ≈ 8.39 bpw: 353 F32 + 187 Q8_0 + 311 `GGML_TYPE_Q8_0_ROCMFPX` tensors — only this fork can read them), **MTP spec decode from the repo's own `mtp-Qwen3.8-27B-Q4_0.gguf` (n-max 4, pinned to ROCm0)**, **1 session × 262,144 (native window)** with **f16 KV**, the bundled BF16 mmproj for vision, thinking on with the card's recipe (temp 1.0 / top_p 0.95 / top_k 20). Measured 2026-10-02: decode **17.0–17.1 t/s** with MTP vs **7.6 without (+123 %)**, draft acceptance 0.462, prefill 325–351 t/s, prompt-cache replay 0.5 s, boot→healthy 16 s, GTT 64.5 GiB. Cut over 2026-10-02 from gufo; rollback tag `frank-large-gufo-q4kxl-20261001` | 7770 |
| Dankclaw | `frank-small` | llama.cpp master, CUDA sm_86 build (root systemd unit, relinked) | bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF **Q5_K_M** (9B dense qwen3_5 hybrid, 6.4 GiB GGUF, no MTP weights so no spec decode) on the RTX 3090, **2 × 262,144** (native window) with **asymmetric KV f16 K + q8_0 V** under flash attention, 20,164/24,576 MiB VRAM, 100.4–101.8 tok/s decode, text-only. Swapped 2026-10-02 from Q6_K; rollback `models/MiMo-Q6K/` | 7771 |
| Dankclaw | `frank-small` | llama.cpp master, CUDA sm_86 build (root systemd unit, relinked) | bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF **Q5_K_M** (9B dense qwen3_5 hybrid, 6.4 GiB GGUF, no MTP weights so no spec decode) + the repo's f16 mmproj for **vision**, on the RTX 3090, **3 × 180,224** (pool 540,672 — NOT the native window; three contexts cost 81,920 tokens per conversation) with **asymmetric KV f16 K + q8_0 V** under flash attention, 21,684 used / **2,441 free** of 24,576 MiB, 106.8 tok/s decode, 3,814 t/s prefill. Resized twice 2026-10-02 (Q6_K → Q5_K_M → 3 slots + vision → this pool); rollback `models/MiMo-Q6K/` or `PARALLEL=2 CTX_SIZE=524288` | 7771 |
Retired endpoints (`frank-npu`, and every prior runtime) are in
the [runbook index](#runbook-index). The full swap history lives in
@@ -243,7 +243,7 @@ merely wedged kernel usually still answers ARP.
1. Stop and mask (self-healing would restart it):
- Dankclaw `frank-large` (root unit, vLLM podman on the iGPU):
`sudo systemctl stop frank-large && sudo systemctl mask frank-large`
- Dankclaw `frank-small` (root unit, SGLang on the 3090):
- Dankclaw `frank-small` (root unit, llama.cpp on the 3090):
`sudo systemctl stop frank-small && sudo systemctl mask frank-small`
- Dankserver `frank-primary` (root unit, tcclaviger vLLM on the 2x R9700):
`sudo systemctl stop frank-primary && sudo systemctl mask frank-primary`
@@ -277,12 +277,15 @@ When renaming a service, retiring an endpoint, or moving a model: update the
provider block, model `id`, `contextWindow`, `maxTokens`, `input` in
`models.yml`, then fix every `modelRoles` entry that named the old endpoint —
a role pointing at a provider the file does not define fails at call time. As
of 2026-10-02 `frank-large` (the BF16 mmproj that ships inside its GGUF
payload) and `frank-primary` (loads the BF16 mmproj explicitly — this build
never autoloads projectors) accept images (`input: [text, image]`);
`frank-small` is **text-only** — the GGUF payload it moved to on 2026-10-02
ships no projector, and the in-checkpoint MiMo vision tower exists only in the
safetensors layouts. Note also that
of 2026-10-02 **all three endpoints accept images** (`input: [text, image]`):
`frank-large` through the BF16 mmproj that ships inside its GGUF payload,
`frank-primary` through a BF16 mmproj loaded explicitly (this build never
autoloads projectors), and `frank-small` through
`mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf` passed as `--mmproj` — the Q5_K_M
GGUF itself carries no vision tensors. `frank-small`'s `contextWindow` is
**180224**, the per-slot window, not the model's native 262144: the harness
must compact before llama.cpp answers a longer prompt with a 400. Note also
that
the reasoning channel differs by runtime: llama.cpp AND SGLang (qwen3
parser, live-verified) emit `reasoning_content`, vLLM emits `reasoning` —
models.yml sets `reasoningContentField` per endpoint to match.
@@ -370,11 +373,14 @@ unit/env/serve trio and redeploy.
**root systemd unit** (`scripts/frank-small.service`, `relink_unit`ed into
`/etc/systemd/system/`), serving
`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` at **Q5_K_M** (6,876,124,704 B,
sha256 `04cf46f2…`) on port 7771. The 2026-10-02 swap was a **quant rung down
from Q6_K**, not a runtime change — the llama.cpp path arrived 2026-10-01
night for one reason: **asymmetric KV**. `--cache-type-k f16` +
`--cache-type-v q8_0` is the fleet's only per-tensor KV lever; SGLang's
`--kv-cache-dtype` moves K and V together.
sha256 `04cf4f…`) **plus `mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf`**
(918,166,048 B, sha256 `ff348f…`), both `/home/dank/models/MiMo-Q5KM/`, on
port 7771. The 2026-10-02 swap was a **quant rung down from Q6_K**, not a
runtime change — the llama.cpp path arrived 2026-10-01 night for one reason:
**asymmetric KV**. `--cache-type-k f16` + `--cache-type-v q8_0` is the fleet's
only per-tensor KV lever; SGLang's `--kv-cache-dtype` moves K and V together.
Vision came the same day via `--mmproj`: the GGUF carries no `v.*` tensors, so
the projector file is the entire vision path (and ~1.4 GiB of the card).
**Architecture: qwen3_5 hybrid** — 32 layers, 8 full-attention + 24 Gated
DeltaNet, native 262,144 window. KV exists only in the 8 attention layers
@@ -382,19 +388,30 @@ DeltaNet, native 262,144 window. KV exists only in the 8 attention layers
the DeltaNet state is a fixed per-slot buffer, so slot count — not context —
drives that term.
**The shape:** `--ctx-size 524288 --parallel 2` = TWO full native windows
(llama.cpp divides the pool: `n_ctx_slot = CTX_SIZE / PARALLEL`),
`--flash-attn on` (the q8_0-V path is only cheap under flash attention — the
seam pins it), f16 K + q8_0 V, all layers on the GPU. Measured VRAM
**20,164 / 24,576 MiB**; full f16 V would add ~3.96 GiB and bust the card,
which is the whole justification for the asymmetric contract.
**The shape:** `--ctx-size 540672 --parallel 3` = **THREE conversations of
180,224 tokens each** (llama.cpp divides the pool:
`n_ctx_slot = CTX_SIZE / PARALLEL`), `--flash-attn on` (the q8_0-V path is
only cheap under flash attention — the seam pins it), f16 K + q8_0 V, all
layers on the GPU, `--mmproj` for vision. Measured VRAM **21,684 used / 2,441
free** of 24,576 MiB — the 2–3 GiB headroom Frank asked for, landed by
solving the pool against two measured boots: non-KV cost is 7,390 MiB without
a projector (2 × 262,144 boot) and 8,859 MiB with one (3 × 196,608 boot), so
vision costs ~1.4 GiB, not the 0.9 GiB the file size suggests. Three native
windows (786,432 tokens) need 18.4 GiB of KV alone and were never on the
table. Full f16 V would add ~4 GiB more and bust the card — that is the
justification for the asymmetric contract.
Verified at deploy (2026-10-02): `/v1/models` reports `frank-small`; decode
**100.4–101.8 tok/s** at temp 0; two concurrent requests answered in
parallel; native `tool_calls`; thinking in `reasoning_content`. **Text-only**
— this GGUF ships no projector, so `models.yml` declares `input: [text]`
(the in-checkpoint vision tower exists only in the safetensors layouts the
SGLang run used).
Verified at deploy (2026-10-02): `/v1/models` reports `frank-small`; boot logs
`n_slots = 3, n_ctx_slot = 180224` and `loaded multimodal model`; decode
**106.8 tok/s** at temp 0 (prefill 3,814 t/s) — ~5 % faster than the
2 × 262,144 shape, because a shorter slot means a shorter attention scan;
three concurrent requests answered in parallel and a fourth queued behind a
released slot; native `tool_calls`; thinking in `reasoning_content`; a
solid-red PNG answers "Red." through an `image_url` content part (the legacy
top-level `images` field is ignored by `/v1/chat/completions` — the model then
invents a color). A prompt over the slot is a **400
`exceed_context_size_error`**, which is why `models.yml` sets
`contextWindow: 180224`.
Runbook: `serverAppConfigs-docs/LLM/frank-small-llama-mimo-q5km.md`.
Rollback: [[frank-small-llama-mimo-q6k]] (Q6_K file on disk at
@@ -701,6 +718,35 @@ linked runbooks.
host-RAM hash table) and its window decides which traffic it can see: too
short and it proposes on ordinary prose fragments, which costs verification
without acceptance.
- **`--parallel` divides the pool, so MORE conversations cost WINDOW, and the
per-slot number is the contract — not `ctx-size`.** `n_ctx_slot =
CTX_SIZE / PARALLEL`, and llama.cpp refuses a prompt longer than the SLOT
(`400 exceed_context_size_error: request (184001 tokens) exceeds the
available context size (180224 tokens)`) even when the pool has room. So
frank-small's 2 → 3 reslice moved each conversation from the model's native
262,144 down to 180,224, and `models.yml` `contextWindow` had to follow or
the harness would discover the ceiling as a 400 on a long conversation. Side
effect worth knowing: the shorter slot made decode ~5 % FASTER (106.8 vs
100.4 tok/s) — less attention to scan and one fewer DeltaNet state resident.
Measured 2026-10-02. See [[frank-small-llama-mimo-q5km]].
- **Price a VRAM budget from two boots, never from a formula — and an mmproj
costs ~1.5x its file size.** The frank-small 3090 fit was solved from two
measurements: 2 × 262,144 with no projector = 20,190 MiB used (KV 12,800 ⇒
**non-KV 7,390**), 3 × 196,608 with the f16 projector = 22,972 used (KV
14,113 ⇒ **non-KV 8,859**). The difference is the vision tower: a 918 MB file
that bills ~1.4 GiB, because the encoder's activation and compute buffers
ride with it. Subtracting the two rows is exact; extrapolating weights+KV+
"a bit for buffers" is what leaves 1.1 GiB free when you meant 2.7. Then
solve `KV ≤ card − non-KV − headroom` and round to a multiple of
`PARALLEL × 4096`. Recorded in `scripts/frank-small.env` so the next resize
starts from measurement.
- **`images` and `image_url` are NOT interchangeable: only the content part
reaches the model.** `--mmproj` loads, `usage.prompt_tokens` rises, and the
model still invents an answer if the client used the legacy top-level
`images` field on `/v1/chat/completions` — frank-small answered "Brown." for
a solid-red PNG that way and "Red." through the OpenAI `image_url` part. The
failure is silent because a hallucinated color is a plausible answer. Probe
vision with a solid color and expect an exact word.
- **`--jinja` is what `tools` needs.** On 2026-09-16 a subagent died with
`500 tools param requires --jinja flag`, from the ik_llama.cpp runtime that
then served frank-large: every tool-using agent on that endpoint was broken
+3 -2
View File
@@ -13,8 +13,9 @@
# KV = 14,113 MiB => non-KV WITH the vision tower = 8,859 MiB
# The tower costs ~1.4 GiB, not the ~1.0 its file size suggested, so 196,608
# per slot left only 1.1 GiB free — outside the band. The pool therefore is
# 3 x 180,224 = 540,672 tokens: KV ~12,936 + non-KV 8,859 = ~21,800 MiB used,
# ~2.7 GiB free, the middle of what was asked for.
# 3 x 180,224 = 540,672 tokens: KV ~12,936 + non-KV 8,859 = ~21,800 MiB predicted.
# MEASURED at this shape: 21,684 MiB used / 2,441 MiB free — inside the band, and
# the number the bats budget gate reproduces (it prices non-KV at 9 GiB).
#
# KV arithmetic: attention KV exists only in the 8 full-attention layers
# (4 KV heads x 256 head_dim). K f16 = 16 KiB/token, V q8_0 = 8.5 KiB/token
@@ -1,7 +1,7 @@
---
type: reference
title: "frank-small on Dankclaw — llama.cpp, MiMo-V2.6-Distill-Qwen-9B Q5_K_M GGUF"
description: "The active frank-small runbook — a CUDA (sm_86) build of llama.cpp master serving bartowski's MiMo-V2.6-Distill-Qwen-9B Q5_K_M GGUF on the RTX 3090: two native-window sessions, the asymmetric KV contract (f16 K + q8_0 V), the VRAM fit, measured rates, and what to check when it misbehaves."
description: "The active frank-small runbook — a CUDA (sm_86) build of llama.cpp master serving bartowski's MiMo-V2.6-Distill-Qwen-9B Q5_K_M GGUF on the RTX 3090: three conversations of 180,224 tokens, mmproj vision, the asymmetric KV contract (f16 K + q8_0 V), the two VRAM measurements that set the pool size, and what to check when it misbehaves."
tags:
- llm
- frank-small
@@ -17,11 +17,17 @@ timestamp: 2026-10-02
# frank-small — llama.cpp MiMo-V2.6 Q5_K_M (RTX 3090)
> **ACTIVE since 2026-10-02.** One quant step down from
> [[frank-small-llama-mimo-q6k]] on the same runtime, the same shape and the
> same KV contract. Frank's order: `bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q5_K_M`
> on llama.cpp, two contexts, whatever context size fits the 3090. The answer
> to "whatever fits" is unchanged — **2 x 262,144**, because 262,144 is the
> model's native window and two of them already fit.
> [[frank-small-llama-mimo-q6k]] on the same runtime and the same KV
> contract, then **resized twice the same day** on Frank's orders: first to
> three contexts with vision, then down to the pool that keeps 2–3 GiB of the
> card free. Frank's arithmetic request — "as much as can fit while keeping
> overhead open space to 2-3gb on the 3090" — is answered by the two
> measurements in *Shape and the KV contract* below; the answer is
> **3 × 180,224**, not 3 × 262,144.
> **The tradeoff is the per-slot window.** Three sessions cost 81,920 tokens
> of per-conversation memory versus the two-slot shape: 180,224 tokens each
> instead of the model's native 262,144. See *What three slots cost*.
## Identity
@@ -29,15 +35,16 @@ timestamp: 2026-10-02
|---|---|
|Endpoint|`192.168.0.143:7771`, served alias `frank-small`|
|Runtime|llama.cpp **master** (qwen35 model support), CUDA build `sm_86`, at `/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server` — unchanged across the quant swap|
|Payload|`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` **Q5_K_M**, 6,876,124,704 B (6.40 GiB), sha256 `04cf46f2b5584ef922697d6c063f94879434fb86c467f1a09e52daf0cddaf35f`, at `/home/dank/models/MiMo-Q5KM/` (fetched by `scripts/download-frank-small.sh`)|
|Payload|`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` **Q5_K_M**, 6,876,124,704 B (6.40 GiB), sha256 `04cf46f2b5584ef922697d6c063f94879434fb86c467f1a09e52daf0cddaf35f` + the repo's `mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf`, 918,166,048 B, sha256 `ff348f3180a63188aa7285db85f550fe38acb61dd013c599eb8bad08d2cc2576` — both at `/home/dank/models/MiMo-Q5KM/`, fetched and byte- and sha256-verified by `scripts/download-frank-small.sh`|
|Unit|root `frank-small.service` (repo `scripts/frank-small.service`, `User=dank`, relinked), env `scripts/frank-small.env`, serve `scripts/serve-frank-small.sh`|
|Repo pins|`tests/frank_small_unit.bats` (stub-argv seam + the VRAM arithmetic), `deploy_llm_small` gates in `scripts/deploy-remote.sh`|
## Shape and the KV contract
`--ctx-size 524288 --parallel 2` — llama.cpp divides the pool across slots, so
each session gets `n_ctx_slot = 262144`, the model's full native window (boot
log confirms `n_slots = 2, n_ctx_slot = 262144, kv_unified = 'false'`).
`--ctx-size 540672 --parallel 3` — llama.cpp divides the pool across slots, so
each conversation gets `n_ctx_slot = 180224` (boot log: `n_slots = 3,
n_ctx_slot = 180224, kv_unified = 'false'`). `CTX_SIZE` MUST stay divisible by
`PARALLEL`; the bats seam enforces it.
The KV contract is Frank's: **K unquantized (f16), V `q8_0`** —
`--cache-type-k f16 --cache-type-v q8_0`. Two hard dependencies ride on it:
@@ -47,29 +54,64 @@ The KV contract is Frank's: **K unquantized (f16), V `q8_0`** —
lie. The serve script pins the trio; the bats seam pins the script.
- The VRAM budget: attention KV lives in the 8 full-attention layers only
(4 KV heads × 256 dim). K f16 ≈ 16 KiB/token, V q8_0 ≈ 8.5 KiB/token
(34 B per 32-element block) ⇒ ~24.5 KiB/token over the 524,288-token pool ≈
**12.5 GiB**. Weights 6.40 + KV 12.5 + Gated DeltaNet state (~25 MiB/slot) +
compute buffers/CUDA graphs ⇒ **20,164 MiB measured** against the card's
24,576 MiB.
(34 B per 32-element block) ⇒ ~24.5 KiB/token over the pool.
**Why the quant step did not buy a bigger window.** The model cannot be run
past its trained 262,144 without scaling, and two of those windows are already
the pool's ceiling. The 0.86 GiB the smaller quant frees is margin, not
geometry. It is also not enough to un-quantize V: f16 V costs 16 KiB/token
instead of 8.5, i.e. +3.96 GiB on this pool, which lands past the card. That
arithmetic is a test (`the KV budget fits the 3090…`), so a future CTX_SIZE or
quant change that breaks the fit fails the suite instead of the boot.
**The pool size came from two boots, not from a formula.** `free`-style
arithmetic on this stack is unreliable because the compute buffers, CUDA
graphs and now the vision tower all bill separately, so the fit was measured
at two shapes and solved between them:
## Measured on this file (2026-10-02)
|Shape|Projector|`nvidia-smi` used|KV at 24.5 KiB/token|Non-KV (weights + buffers + projector)|
|---|---|---|---|---|
|2 × 262,144 (pool 524,288)|none|20,190 MiB|12,800 MiB|**7,390 MiB**|
|3 × 196,608 (pool 589,824)|f16|22,972 MiB|14,113 MiB|**8,859 MiB**|
The two rows price the projector and its image buffers at **~1.4 GiB** — not
the ~0.9 GiB the 918 MB file suggests, because the vision encoder's own
activation and compute buffers ride along. Solving the second row's non-KV cost
against Frank's 2–3 GiB band: the card is 24,576 MiB, so
`KV ≤ 24,576 − 9,216 − 2,048 = 13,312 MiB` ⇒ pool ≤ 556,630 tokens, rounded
down to a multiple of 3 × 4,096 ⇒ **540,672**, i.e. 13,265 MiB of KV and
**2,441 MiB free** (measured, 21,684 MiB used).
**Why 3 × 262,144 was never possible.** Three native windows are 786,432
tokens = 18.4 GiB of KV *alone*; plus the 8.86 GiB of non-KV cost that is
27.3 GiB on a 24 GiB card. The quant step bought 0.86 GiB; the geometry needed
6 GiB.
## Measured on this shape (2026-10-02, 3 × 180,224 + vision)
|Check|Result|
|---|---|
|Boot to healthy|~20 s, `n_slots = 2`, `n_ctx_slot = 262144`|
|VRAM|20,164 MiB / 24,576 MiB (`nvidia-smi`)|
|Decode, temp 0, no thinking|**100.4–101.8 tok/s** (256–512 token completions at ~900-token depth)|
|Two concurrent requests|both answered, 256 tokens each in 3.2 s wall (slots are real)|
|Tools|native `tool_calls` with correct arguments (`get_weather {"city":"Seattle","unit":"c"}`)|
|Thinking|`reasoning_content` populated on a reasoning-requiring prompt|
|Boot to healthy|~20 s, `n_slots = 3`, `n_ctx_slot = 180224`, `loaded multimodal model` logged|
|VRAM|**21,684 MiB used / 2,441 MiB free** of 24,576 (`nvidia-smi`, steady state; the KV pool is allocated whole at boot, so this is the ceiling)|
|Decode, temp 0, no thinking|**106.8 tok/s** (256-token completion at ~1,300-token depth)|
|Prefill|**3,814 tok/s** (same request)|
|Three concurrent requests|all three answered in 1.7 s wall — real parallel slots|
|Fourth concurrent request|answered in 3.0 s while three ran — queued behind a released slot, as the slot ceiling requires|
|Vision|`image_url` part with a 64×64 solid-red PNG → **"Red."**|
|Tools|native `tool_calls` with correct arguments (`get_weather {"city":"Seattle","unit":"f"}`)|
|Thinking|`reasoning_content` populated (145 chars on a "think first" prompt); `reasoning_effort: "low"` accepted|
|Over-slot prompt|`400 exceed_context_size_error: request (184001 tokens) exceeds the available context size (180224 tokens)` — refused, never silently truncated|
Decode is ~5 % **faster** than the 2 × 262,144 shape measured earlier the same
day (100.4–101.8 tok/s): a shorter per-slot window means a shorter attention
scan at equal prompt depth, and one less slot's DeltaNet state resident.
## What three slots cost
The per-slot window is the whole price. Two consequences the fleet depends on:
- **A prompt over 180,224 tokens is a hard 400**, so `~/.omp/agent/models.yml`
sets `contextWindow: 180224` for this endpoint — the harness compacts before
it reaches the ceiling instead of discovering it.
- **frank-primary's compaction prompt no longer fits with room to answer.**
Its trigger is 183,501 tokens — already above this slot — and its summary
budget is 32,768 output tokens. On the two-slot shape (262,144) that left
32.8k of output room, which is why that number was chosen; on this shape a
single compaction request must be truncated or split. Nothing in the fleet
routes compaction here automatically, but any runbook that claims frank-small
"fits a compaction request in ONE request" is describing the 262,144 shape.
## Client contract
@@ -81,10 +123,17 @@ quant change that breaks the fit fails the suite instead of the boot.
`extraBody`. A raw client that omits it gets a direct answer — measured, and
not a fault. Trivial questions produce no thinking even with the flag on.
- Tools: `--jinja` + server-side parsing, verified 2026-10-02.
- **Text-only.** This GGUF carries no vision tensors. The HF repo does ship a
paired `mmproj-MiMo-V2.6-Distill-Qwen-9B-bf16.gguf`, so vision is one
`--mmproj` away if it is ever wanted here (frank-primary is the fleet's
vision endpoint today).
- **Vision is on**, through the projector file (`--mmproj`). Use the
OpenAI-standard `image_url` content part — that path answers correctly
("Red." on a solid-red PNG). The legacy top-level `images` field is **not**
read by `/v1/chat/completions`: the same request sent that way answered
"Brown.", i.e. it generated from text alone. `/completion` takes `images`
but refuses a request without `messages` in this build, so chat is the only
vision path.
- Setting `MMPROJ_FILE=` (explicitly empty) in `scripts/frank-small.env` drops
`--mmproj` and returns ~1.4 GiB of the card to KV — the documented text-only
opt-out. `MMPROJ_FILE` pointing at a file that does not exist is a boot
refusal, never a silent fallback to blindness.
## No spec decode
@@ -98,7 +147,9 @@ traffic first. The bats seam fails if a draft flag ships unannounced.
|Target|How|
|---|---|
|Q6_K, same runtime|`scripts/frank-small.env` `MODEL_FILE=/home/dank/models/MiMo-Q6K/MiMo-V2.6-Distill-Qwen-9B-Q6_K.gguf` (7,793,710,624 B, still on disk) and the deployer's `LLM_SMALL_MODEL_FILE` default|
|Q6_K, same runtime|`scripts/frank-small.env` `MODEL_FILE=/home/dank/models/MiMo-Q6K/MiMo-V2.6-Distill-Qwen-9B-Q6_K.gguf` (7,793,710,624 B, still on disk) and the deployer's `LLM_SMALL_MODEL_FILE` default. Q6_K needs ~1.4 GiB more than Q5_K_M, so pair the rollback with `CTX_SIZE` 49,152 tokens lower or `MMPROJ_FILE=`|
|Two 262,144 windows|`CTX_SIZE=524288 PARALLEL=2` (measured 20,190 MiB used, 4,386 free) and `contextWindow: 262144` in `models.yml` — choose it when a single conversation needs the native window or when frank-primary's compaction request has to land here|
|Vision off|`MMPROJ_FILE=` in the env file; the deployer gate then has to be satisfied too, so point `LLM_SMALL_MMPROJ` at any real projector file or drop the gate in the same commit|
|SGLang + INT8|[[frank-small-sglang-mimo-v26]] — venv and old unit/env/serve trio still in place; the INT8 payload was staged to `/home/dank/.AGENTTRASH/20261002-retired-llm-payloads/` on 2026-10-02, so restore it there or re-fetch with `old/download-frank-small-sglang-int8.sh`|
## When it misbehaves
@@ -107,7 +158,14 @@ traffic first. The bats seam fails if a draft flag ships unannounced.
naming the missing file if the binary or GGUF moved.
- `No CUDA GPUs are available`: the 3090 vanished from the bus or the UVM node
is missing — `nvidia-modprobe -u -c=0` (the serve script runs it).
- Requests queue behind each other: two concurrent long generations are the
slot ceiling by contract.
- Requests queue behind each other: three concurrent long generations are the
slot ceiling by contract; a fourth waits for a slot to free (measured: 1.7 s
for the three, 3.0 s for the fourth).
- `400 … exceeds the available context size (180224 tokens)`: the conversation
outgrew ONE slot. This is not pool exhaustion — the pool is 540,672 tokens
and the per-slot window is the binding limit. Shorten the prompt, compact, or
roll back to the 262,144 shape above.
- Vision answers a color it never saw: the client sent `images` instead of an
`image_url` content part (see *Client contract*).
- Slow decode after a CTX or quant change: check `nvidia-smi` — pushing KV out
of VRAM into unified memory halves the rate silently.
@@ -52,7 +52,10 @@ timestamp: 2026-10-01
trigger 183,501 + a 32,768-token summary budget = 216,269) now fits
one request. A single request may grow to the whole pool — a
170,019-token prompt was verified at the 4-slot shape — the grid is
the two-way concurrency contract.
the two-way concurrency contract. **That compaction arithmetic is
specific to a 262,144-token slot**: the ACTIVE shape runs three slots of
180,224, which 183,501 tokens already exceeds — see *What three slots
cost* in [[frank-small-llama-mimo-q5km]].
- **KV:** `fp8_e4m3` — 16 KiB/token (8 attn layers x 2 x 4 heads x 256
dim x 1 B), pool 9.31 GiB.
- **The memory shape (the 4.5 GiB the naive math misses):** the DeltaNet
+3 -2
View File
@@ -76,8 +76,9 @@ frank-small, port 7771]
- [[frank-small-llama-mimo-q5km]] — **ACTIVE since 2026-10-02** (root
systemd unit, port 7771): llama.cpp master (CUDA sm_86) serving
bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF Q5_K_M on the RTX 3090,
2 x 262,144 with K f16 + V q8_0, 100.4-101.8 tok/s decode, 20,164 MiB
measured. The KV arithmetic, the flash-attn dependency, rollback to Q6_K
THREE slots of 180,224 tokens (pool 540,672) with K f16 + V q8_0 and the
mmproj for vision, ~100 tok/s decode, ~2.7 GiB of the card left free.
The KV arithmetic, the flash-attn dependency, rollback to Q6_K
- [[frank-primary-glm53-flash-w4a16]] — RETIRED 2026-09-30 → 10-01 (the
- failed GLM experiment; frank-primary runs Qwen FP8 again, see the intro):
(2026-09-30, root systemd unit, port 7770): the tcclaviger RDNA4 vLLM fork
+3 -2
View File
@@ -285,8 +285,9 @@ The LLM inference fleet on the two hosts. Full index:
- [[frank-small-llama-mimo-q5km]] — the ACTIVE frank-small runbook since
2026-10-02: a CUDA (sm_86) build of llama.cpp master serving
bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF **Q5_K_M** on Dankclaw's RTX
3090, port 7771 — 2 x 262,144 native windows, the asymmetric KV contract
(f16 K + q8_0 V) and the VRAM arithmetic behind it, ~100 tok/s decode
3090, port 7771 — THREE conversations at 180,224 tokens each, the
asymmetric KV contract (f16 K + q8_0 V), vision through the repo's mmproj,
and the two VRAM measurements that set the pool size; ~100 tok/s decode
- [[frank-small-llama-mimo-q6k]] — RETIRED 2026-10-01 (same day; replaced
by [[frank-small-llama-mimo-q5km]], payload still on disk): the first
llama.cpp + GGUF shape for this endpoint, Q6_K at the same
+68
View File
@@ -925,3 +925,71 @@ any repo service.
`~/.AGENTTRASH/20261001-frank-large-reclaim/` — 52 G freed. KEPT the
1.1 G `Qwen3.8-27B-DFlash2-Q4_K_M.gguf` (it is the gufo rollback's
drafter, not a remnant).
## 2026-10-02 — frank-large cutover: vLLM MiMo -> ROCmFPX Q8_0_ROCMFPX_AGENT + MTP
Frank's order was the highest quality of
`kingjones777/Qwen3.8-27B-ROCmFP4-STRIX-MTP-GGUF` on one full context, "whatever
runtime it requires". The answer is forced by the file: `Q8_0_ROCMFPX_AGENT.gguf`
carries tensor type 103 (`GGML_TYPE_Q8_0_ROCMFPX`, ftype 115), which stock
llama.cpp, gufo and every other loader on the fleet cannot read — only the
kingjones30/ROCmFPX fork can, so the runtime is pinned to commit
`36e9acd40e10a87cd3c3ef8ec734668757dc8520` and built by
`scripts/build-rocmfpx.sh`. Shape: 1 session x 262,144 (native), f16 KV, the
bundled BF16 mmproj, and MTP from the repo's own `mtp-Qwen3.8-27B-Q4_0.gguf`
(n-max 4, `--spec-draft-device ROCm0`).
- **MTP is the whole performance story: 17.0-17.1 t/s with, 7.60-7.66 without**
(+123 %), one session, temp 0, production stopped per arm, acceptance 0.462 /
mean length ~1.86, at an 8-15 % prefill tax (325-351 vs 381 t/s). The AGENT
routing variant was chosen over the card's plain Q8_0 because it keeps 187
output-head tensors at plain Q8_0 instead of 2, which measured MTP acceptance
0.953 vs 0.911.
- Steady GTT occupancy 63.1 GiB of the ~114 GiB aperture; boot to healthy 16 s.
- Rollback: git tag `frank-large-gufo-q4kxl-20261001`. The retired vLLM-era
fetchers moved to `old/`. Runbook
[[frank-large-rocmfpx-q8agent-mtp]].
## 2026-10-02 — frank-small: Q5_K_M, then three contexts + vision, sized by two boots
Three orders in one day on the same endpoint: `bartowski/…Q5_K_M` on llama.cpp
with two contexts ("whatever context size fits the 3090"), then **three
contexts** "as much as can fit while keeping overhead open space to 2-3gb on
the 3090", then **"frank-small should have vision"**.
- **Final shape: `CTX_SIZE=540672 PARALLEL=3` = 3 x 180,224**, f16 K + q8_0 V
under flash attention, `--mmproj` vision, all layers on the 3090. Measured
**21,684 MiB used / 2,441 MiB free** of 24,576 — inside the 2-3 GiB band.
- **The pool came from two boots, not a formula.** 2 x 262,144 with no
projector: 20,190 MiB used, KV 12,800 => non-KV **7,390 MiB**. 3 x 196,608
with the projector: 22,972 used, KV 14,113 => non-KV **8,859 MiB**. The
difference prices the vision tower at **~1.4 GiB for a 918 MB file** — the
encoder's own buffers ride with it. The first 3-slot deploy (3 x 196,608)
booted healthy but left only 1,153 MiB free, which is how the band got
violated and why the seam now prices non-KV at the measured 9 GiB and
REJECTS that shape.
- **Three contexts cost window, and that is the tradeoff Frank was told:**
180,224 tokens per conversation, not the native 262,144. A longer prompt is
a hard `400 exceed_context_size_error`, so `models.yml` sets
`contextWindow: 180224`. Consequence: frank-primary's 183,501-token
compaction request no longer fits here with summary room; the two-slot
rollback is the way back.
- Measured (2026-10-02, this shape): decode **106.8 t/s**, prefill **3,814
t/s** (~5 % faster decode than 2 x 262,144 — shorter slot, shorter attention
scan), three concurrent requests answered in parallel in 1.7 s with a fourth
queued at 3.0 s, native `tool_calls`, `reasoning_content` populated,
`reasoning_effort: low` accepted, solid-red PNG answered **"Red."**
- **Vision contract gotcha:** `/v1/chat/completions` ignores the legacy
top-level `images` field — that request answered "Brown." for the same red
image, i.e. it generated from text alone. Only the `image_url` content part
reaches the model. `/completion` accepts `images` but this build still
requires `messages`.
- Deployer gates on the projector too (`LLM_SMALL_MMPROJ`), so a missing
mmproj cannot silently ship a blind endpoint; `MMPROJ_FILE=` (explicitly
empty) in the env file is the documented text-only opt-out and returns ~1.4
GiB to KV.
- Reclaim staged, **not purged, awaiting approval**:
`~/.AGENTTRASH/20261002-retired-llm-payloads/` (~59 GiB of superseded
payloads). Kept in place for rollback: `MiMo-Q6K/`, `MiMo-Q5KM/`,
`Qwen3.8-27B-UD-Q4_K_XL/` and `Qwen3.8-27B-UD-Q8_K_XL/` (the latter holds the
gufo DFlash2 drafter). Runbook [[frank-small-llama-mimo-q5km]].