CTX 262144 / PARALLEL 2 = two 131,072 conversations; SPEC_TYPE=none strips both speculative flags in the serve script (ngram-mod skipped on purpose — on a qwen35 hybrid it disables recurrent-state rollback); ubatch 1024->512 claws back ~0.3 GiB. Budget: non-KV ~15.5 GiB + two q8_0 slots 8,704 MiB lands AT the card — the 2-3 GiB floor is knowingly violated by explicit order; nvidia-smi free at boot is the measured truth. Seam retargeted: shape (2 slots, 131072/slot), budget (two fit, three don't), spec-off (absence of both flags), unit Description.
The parallel session's uncommitted swap, committed at Frank's direction
(commit whatever's in the working tree):
- llama.cpp master on the RTX 3090 now serves ukisai's
Swift-1.5-Qwen3.8-27B GSQ-RCO IQ3_S WITH the in-file MTP head, ONE
131,072-token slot at Frank's 128k cap, q8_0 K + q8_0 V, vision through
the base-model projector; MiMo-V2.6 Q6_K (2x200704 f16 KV) retired to
rollback target.
- env/serve/download + seam twins updated together (deployer payload gates
rode the previous commit's deploy-remote.sh).
- docs: new swift15 runbook; MiMo Q6K runbook retired; AGENTS.md topology
and both vault indexes follow (root index ACTIVE/RETIRED flip, test 135
seam green).
- comfyui: two qwen-image t2i workflow drops under dankclaw/comfyui/
workflows/ (12 KiB).
BATCH_SIZE 2048 -> 4096, UBATCH_SIZE 1024 -> 2048.
Why the ubatch move is right on THIS card: the prefill curve is
card-specific. On the 2x R9700 layer-split sibling it peaks at ubatch 512
and every step up costs GiB for no speed; on this CUDA 3090 it climbs
monotonically to 2048 (710 -> 1188 t/s on the 35B MoE sweep, 1944 -> 2423
t/s on the Ornith 8k sweep) with decode flat to under 1 t/s on every rung.
The logical batch is free either way: two sweeps agree that at a fixed
ubatch, 2048/4096/8192 give identical prefill speed and identical peak
VRAM, because the compute buffers are sized by the ubatch alone.
The risk is all in the ubatch, and it is documented rather than assumed:
the step cost ~1,244 MiB of peak graph VRAM on the 35B MoE, the shape this
replaces is 2,815 MiB free, and the Q5_K_M predecessor recorded "ubatch
2048 failed the scratch boot" with MORE free than that. Compute buffers
are allocated at graph capture during boot, so a failure is loud and fast;
the step-down rungs (4096/1536, then 4096/1024) are named in the env.
The seam stops hard-coding a rung: serve_argv now carries BATCH_SIZE and
UBATCH_SIZE the way it already carried CTX_SIZE et al., and the pair test
asserts the argv follows the shipped env plus the llama.cpp invariant
ubatch <= batch. Verified both ways: retuning the env to 8192/4096 keeps
the seam green, inverting the pair to 2048/4096 turns it red.
The freed third slot funded exact V; the remaining ~3.5 GiB of honest
nvidia-smi free (3,571, not the 4,046 total-minus-used I quoted - the
driver holds ~450 MiB of reserve) funds the +875 MiB of Q6_K weights.
Projected landing ~2.7 GiB free, inside the 2-3 GiB floor; measure on
boot, step the pool one 12,288-token increment if it lands under 2 GiB.
- payload dir MiMo-Q6K/ made self-contained (mmproj hardlinked from
MiMo-Q5KM/, link-count-2 verified)
- env/serve/deployer/bats repointed; download script now fetches and
verifies Q6_K (7,793,710,624 B, ef96d05a...) with Q5_K_M documented as
the rollback; payload sha verified live on the host before deploy
- seam green 14/14 against the stub engine
Frank's order, two messages: "extend the max context of frank-small to
200700 per individual stream without further quantization", then
"reduce it to 2 streams for a total of 401400-ish tokens, set ubatch to
1k", then "unquantize the KV if we have extra space".
CTX_SIZE 540672 -> 401408 with PARALLEL 3 -> 2 gives n_ctx_slot 200,704:
above the 200,700 the harness compacts at, so a full-length request
lands in one stream instead of a 400 exceed_context_size_error. The pool
must divide evenly across slots and stay 256-aligned for the FA path,
which is why it is 401,408 and not 401,400.
CACHE_TYPE_V q8_0 -> f16: the freed third slot is exactly what pays for
unquantized values (24.5 -> 32 KiB/token), and the budget lands back in
the 2-3 GiB band Frank set on 2026-10-02 instead of leaving ~6 GiB of
the card idle. The asymmetric-KV contract that created this endpoint is
spent with it; both --cache-type flags stay explicit.
--batch-size/--ubatch-size are now passed (2048/1024). The endpoint had
never passed either, so it ran on engine defaults nobody wrote down.
Franksdank-Runbook: serverAppConfigs-docs/LLM/frank-small-llama-mimo-q5km.md
Measured after the first 3-slot deploy: 3 x 196,608 booted (n_slots=3,
n_ctx_slot=196608, multimodal loaded) but used 22,972/24,576 MiB - 1,153 MiB
free, outside the 2-3 GiB band. The two measurements now recorded in
frank-small.env price the vision tower at ~1.4 GiB (non-KV 7,390 -> 8,859
MiB), so the pool drops 49,152 tokens (16,384/slot, 1,176 MiB) to land ~2.7
GiB free. The bats budget gate is recalibrated to the measured 9 GiB non-KV
cost, which now rejects the 3 x 196,608 shape instead of blessing it.
CTX_SIZE 589824 / PARALLEL 3 -> three independent 196,608-token conversations
(a mamba hybrid cannot share one KV cache, so per-slot window = pool/slots;
3 x 262,144 would need 18.4 GiB of KV alone and busts the card).
Vision comes back via bartowski's mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf
(918,166,048 B, sha256 ff348f31..., fetched + verified by
scripts/download-frank-small.sh): the Q5_K_M GGUF ships no v.* tensors, so
--mmproj IS the image path. MMPROJ_FILE without a colon default so an explicit
empty value is a real text-only opt-out, and the deployer now gates on the
projector exactly as the serve script does.
Budget is measured, not guessed: the 2 x 262,144 shape used 20,190/24,576 MiB,
which prices non-KV cost at 7,390 MiB. Three slots + projector land near
22.5 GiB, i.e. ~2 GiB spare - the bottom of the requested band, to be
confirmed by measurement and laddered if it lands outside 2-3 GiB.
One quant step down from Q6_K on the same CUDA build and the same shape:
2 x 262,144 with K f16 + V q8_0. The 0.86 GiB freed from the weights is
headroom, not a new window — the native 262,144 cap is already reached,
and f16 V would still cost 3.96 GiB more than the pool can carry.
The KV pool stays pinned at 606,208 tokens (card is full: 531 MiB free at
the old shape; autofit's 687,388 would leave 0.53 GB and OOM on the first
long request). 2 x 262,144 = 524,288 leaves ~82k tokens of pool slack.
Motivation: harness compaction. frank-primary's contextWindow 262,144
compacts at 70% = 183,501 tokens, and the compaction request carries a
32,768-token summary budget -> 216,269 must fit ONE frank-small request.
The 151,552 slot window could not hold it; the native window does.
No spec-decode interaction: checkpoint ships zero mtp.* tensors (config
lies), seam keeps every --speculative-* flag away from the engine.
Replaces llama.cpp Gemma4-12B-QAT (payload + binary retained as rollback).
SGLang in a self-contained python3.12 venv with an extracted CUDA 13.0
nvcc toolchain for the sgl-kernel/flashinfer JIT.
Shape: fp8 KV, pinned 606,208-token pool / 4 x 151,552 slots, bf16 mamba
states pinned at 32 slots (the engine autofills the ssm pool to a fixed
~5.1 GB regardless of dtype), eager prefill (no headroom for graph
capture), no spec decode (the checkpoint ships no mtp.* tensors despite
mtp_num_hidden_layers=1 — NEXTN drafts with an uninitialised 4.24 GiB
ghost at 0% acceptance).
Verified: 83.9 tok/s decode, 170k single prompt, 4 concurrent 54k
requests, vision (in-checkpoint tower), reasoning_content field.
The disk was at 93% with 137G free. Nine retired payloads are now moved
to ~/.AGENTTRASH/20260928-retired-models/ with a MANIFEST.md giving a
restore command per item — moved, not deleted, and nothing is freed until
the purge is approved. Every one maps to a runtime already retired in the
runbook index.
Only the .gguf was taken from Qwen3.8-27B-UD-Q8_K_XL/: the live
frank-large DFlash2 drafter lives in that directory and stays put. The
manifest warns against restoring the directory wholesale.
Kept: checkpoints/ (23G) — unreferenced by the repo, but ComfyUI is live
and its workflows were not searched, so it is not confirmed dead. Also
logs/, because x4e-run.log is a SPARSE file (3.3G apparent, 4K real) and
frees nothing.
The reference check caught a live bug: serve-frank-small.sh's fallback
MODEL_DIR still pointed at the retired GSQ-RCO payload frank-small served
on 2026-09-16/17. frank-small.env has always overridden it, so the drift
was invisible — but a missing or unreadable env file would have turned a
config problem into a "missing asset" naming a model that is no longer
there. Repointed at Gemma4-12B-QAT/, which the env file already sets.
Not taken, by request: podman images (279G reclaimable, 95%) and the
uv+pip caches (73G). Together 352G of regenerable garbage, larger than
what was reclaimed here.
The 29.3G payload rollback target went with the rest, so the payload-only
rollback is now a re-fetch first; the runbook and AGENTS.md say so.
The build default is 8192 MiB. dankclaw is memory-tight (frank-large's
70 GiB GTT plus the rest of the stack) and Gemma4 runs fully on the 3090
(-ngl 999), so the CPU-side cache is not the hot path. Cap it at 2048 to
give back ~6 GiB of LPDDR. Gemma4 is not a DeltaNet model, so the
>12.8 GiB CACHE_RAM floor does not apply.
Wired through frank-small.env (CACHE_RAM) -> serve-frank-small.sh
(--cache-ram), with a wiring seam so the cap cannot be silently unwired.
The vLLM detour (a2b39b1, same day) is retired: the Q4_K_M llama.cpp setup
is the requested shape and the vLLM KV-quant options on sm_86 all measured
dead (fp8 hardware-impossible, int8 page-size unification, turboquant
mm-prefix). Root unit restored, vLLM quadlet/Dockerfile/fetch script moved
to .AGENTTRASH, runbook listed as same-day-reverted. Rollback target:
frank-small-vllm-gemma4.
int8_per_token_head: page-size unification fails (scale tensor breaks
the 256/512 head-dim ratio). turboquant_k8v4: backend validation fails
(no mm-prefix attention, which Gemma4's vision/audio path requires).
fp8: hardware-dead on sm_86. bf16 it is; history documented in
frank-small.env, seam pins auto + no backend pin.
The VLLM_ATTENTION_BACKEND env var does not exist in this vLLM dev
build (warns 'Unknown vLLM environment variable' and is ignored), so
the pin landed nowhere and Gemma4's hook force-selected TRITON_ATTN
again. Pass --attention-backend TURBOQUANT as a CLI flag instead.
Runtime swap on the RTX 3090 (port 7771):
- vLLM gemma-x86_64-cu129 dev build, digest-pinned, audio extra pip-installed
into localhost/vllm/vllm-openai:frank-small (Dockerfile, no COPY lines)
- google/gemma-4-12B-it-qat-w4a16-ct (10.26 GiB int4) + Google's bf16 MTP
assistant (845 MB) — the w4a16-ct assistant cannot draft in vLLM (drafter
path skips Marlin; acceptance 1.00), bf16 measures 2.80-3.56
- root systemd unit -> rootless Quadlet (dankclaw/frank-small/quadlet/);
manual CUDA device+lib mounts (no nvidia-container-toolkit on host)
- bf16 KV (fp8 hardware-dead on sm_86), 289,647-token pool, 2 concurrent
full-context sessions, CUDA graphs on
- native text+image+audio: the fleet's multimodal endpoint
- measured: pp@67k 1293 t/s, pp@213k 608 t/s, tg@213k ~13.6 t/s,
tg@85k ~17.6 t/s
- host driver 610.57.04 (Ubuntu 26.04); sm_86 is correct, no arch override
- planner/scoping: frank-small now selected by dankclaw/frank-small/ +
env/serve/download trio; old unit retired to scripts/.AGENTTRASH/
- docs: new runbook frank-small-vllm-gemma4.md; AGENTS.md, LLM index,
top docs index updated; old runbook moved to retired
Both were implicit build defaults (3 and 50); the throughput sweep needs
to move them without editing the exec line each time. Defaults preserve
current behavior exactly. Additive only.
CTX_SIZE=1048576 / PARALLEL=4: every slot gets the model's full trained
window. Measured fit arithmetic on the 3090: 1 full-context slot idles
at 11,996 MiB, 4 at 22,346 -> 3,450 MiB per slot, ~8,546 MiB fixed.
A 5th slot projects 25.8 GiB past the 24,576 MiB card, and the KV pool
allocates upfront, so 4 is the ceiling. Explicit CTX_SIZE bypasses --fit
on purpose: fit maximises the TOTAL pool and would divide a smaller
window across slots at parallel>1. The slot-grid seam now pins both the
full-window division and the budget inequality so a future capacity
bump fails pre-deploy, not in the boot allocation.
Audio verified while measuring: the mmproj's audio encoder transcribes
input_audio parts verbatim (JFK WAV sample). Header comments updated.
The Qwen3.8-27B IQ3_S file carried its own MTP head; Gemma4's ships as a
separate assistant file (mtp-gemma-4-12B-it.gguf, arch gemma4-assistant,
4 nextn layers) that loads via --model-draft and shares the target's last
layers' KV. The draftless ngram-mod matcher chains behind it in the same
--spec-type value — two context-backed drafters cannot share one process,
so this chain is the only shape that carries both.
KV is q8_0 (16 KiB/token/layer vs 32 at f16) and CTX_SIZE is unset so
--fit sizes the pool against what the 7.4 GiB of payloads leave on the
24 GiB card. build-cuda was rebuilt with -DGGML_CUDA_FA_ALL_QUANTS=ON,
which quantized KV on CUDA requires. The three seams that pinned the Qwen
contract (MTP-in-file, the Qwen mmproj name, the f16 KV arithmetic) now
pin the Gemma4 contract instead, and the deploy handler's presence check
points at the new payload.
Dankclaw's endpoints are re-split. frank-large is the halogen-flash-server
quadlet again -- the 2026-09-13 unit file was never touched by the two-day
ik_llama.cpp interlude, so the runtime was still in the repo and its image is
still in local storage -- retuned to the shape asked for: HALOGEN_CTX 200192
with a 400,384-position pool and two slots, exactly two 200k conversations.
frank-small is new: mainline llama.cpp on the RTX 3090 serving
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF at IQ3_S, using the -mtp build (it carries
blk.64.nextn.*, which is what lets --spec-type draft-mtp run on the main
model's own weights instead of a second 12 GiB copy) plus the BF16 vision
projector, as one 131,072-token slot with f16 KV on port 7771.
The host is shared now, and that is what the halogen retune pays for:
HALOGEN_MAX_TOK 24576 (arena 20.6 -> 16.4 GiB, ~4-5% prefill) and the vision
tower dropped (1.97 GiB) hand ~6 GiB back to the host, and the reserve rises to
20 to keep the fit check honest. Vision moved wholesale to frank-small.
Retired to scripts/.AGENTTRASH/: the ik trio (env, service, serve script), the
Qwen-Sharp template only that runtime read, and four benchmark harnesses bound
to retired runtimes. Because retiring a file makes any test that greps it pass
by absence, the seams assert the archive exists AND the live path does not.
The RTX 3090 left Dankserver; two AMD Radeon AI PRO R9700 cards (Navi 48,
gfx1201, 32,624 MiB each) replaced it. FreeToken/CUDA cannot run there at all,
so the `frank-small` service is retired and `frank-medium` takes the endpoint.
New service: charlie12345/ROCmFPX llama.cpp (built for gfx1201 against the
fork's self-contained ROCm 7.14 toolchain, so the host's stale /opt/rocm-6.3.3
never participates) serving kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF --
a Q8_0_ROCMFPX_AGENT (ftype 115) checkpoint that stock llama.cpp cannot load.
Placeholder config: --split-mode tensor across both cards, 131072 context,
1 slot, f16 KV, MTP draft head on ROCm0.
deploy-remote.sh grows retire_root_llm_small(): the CUDA-era unit is stopped,
disabled and moved to ~/.AGENTTRASH/20260915-frank-small-retire/host-units
before frank-medium starts, because `systemctl disable` has been observed to
delete the unit file itself on this host (2026-08-26 incident).
The retired frank-small repo files (env, unit, serve script) move to
scripts/.AGENTTRASH/, and the planner, dispatcher, service allow-list and the
deployment test seams now name frank-medium.
frank-default-large -> frank-large (Dankclaw :7770), frank-default-small ->
frank-small (Dankserver :7770), frank-default-sightsound -> frank-npu
(Dankclaw :7771). Units, env files, serve scripts, deploy-plan mappings,
deploy-remote dispatch (stop/disable old units + relink + enable new), and the
test seams move together. Retired frank-default-npu.* artifacts moved to
scripts/.AGENTTRASH/ (tracked-in-history; ignored on disk).
Served aliases become frank-large / frank-small via --alias; the NPU endpoint
serves the whole FLM registry, so its rename changes no served tags.
Two live-verified loader failures on Dankserver (2026-09-01), each a
clean exit at load (no swap-thrash, 37 GB fits RAM):
1. ornith-ai/Ornith-1.5-35B-A3B-FP8 (compressed-tensors PER-CHANNEL fp8):
ValueError: Missing MoE expert source layers: {'gate_up': [0, 1, ...]}
— nonstandard expert tensor naming, provider finds no expert sources.
2. lamianlbe/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-fp8
(compressed-tensors BLOCK-fp8 128x128, standard per-expert naming):
RuntimeError: The size of tensor a (2048) must match the size of
tensor b (...) in _dequant_fp8_weight — the dequant path multiplies
weight * weight_scale directly and cannot expand compressed-tensors
block scales.
Empirical matrix for FreeToken v0.1.2 on qwen3_5_moe (RTX 3090):
modelopt block-fp8 (quant_method 'fp8' + weight_scale_inv) -> LOADS
compressed-tensors per-channel FP8 -> fails
compressed-tensors block-fp8 128x128 -> fails
batsclamp/Huihui-...-abliterated-FP8 is the proven uncensored checkpoint;
restoring it until a modelopt/NVFP4 requant of a wanted model exists or
FreeToken learns compressed-tensors.
Ornith-1.5-35B-A3B-FP8 failed to load on FreeToken v0.1.2: its
compressed-tensors checkpoint uses a nonstandard expert tensor layout
(FreeToken: 'Missing MoE expert source layers: gate_up'). Retarget to
the HauhauCS Aggressive FP8 finetune of the SAME Qwen3.6-35B-A3B arch
Huihui used, which carries compressed-tensors BLOCK-fp8 (128x128) with
the standard per-expert naming (mlp.experts.N.gate_proj/up_proj/down_proj
+ .weight_scale) that FreeToken's MoE loader reads.
- MODEL_DIR -> /home/dank/models/hauhaucs-qwen36-35b-a3b-aggressive-fp8
- use the checkpoint's own chat_template.jinja (drop the Sharp override;
forcing a template the finetune wasn't trained on risks format drift)
- pool plan unchanged (same ~35 GB FP8 mass / qwen3_5_moe arch)
- MODEL_DIR -> /home/dank/models/ornith-1.5-35b-a3b-fp8 (37 GB,
compressed-tensors per-channel FP8, same qwen3_5_moe arch/mass as
Huihui so the pool plan carries unchanged)
- CHAT_TEMPLATE_FILE -> scripts/chat-template.jinja (Qwen-Sharp, shared
with frank-default-large); the checkpoint's stock template
mis-serializes reasoning per its own model card
- env/serve/unit headers updated; loader-risk + BF16-does-not-fit note
- same qwen3_5_moe arch and native block-fp8 format (e4m3, 128x128
weight_block_size, un-fused per-expert layout) as the official
Qwen FP8 checkpoint: identical FreeToken loader path, pool numbers
unchanged (2048 MoE slots / 453005 KV tokens / 4 requests)
- checkpoint downloaded to /home/dank/models/huihui-qwen36-35b-a3b-abliterated-fp8
(26 shards, ~35G); official checkpoint kept for rollback
- ships froggeric fixed Qwen3.6 chat template, read from checkpoint
- also refresh both .service Descriptions (large still said STRIX_LEAN)
--ctx-size is the total pool divided among --parallel slots, so with
PARALLEL=3 the 160k target must be a 491520 pool (163840/slot) to
actually deliver 160k context per slot. Env, serve default, bats updated.
Reconfigure to 3 parallel 160000-ctx slots (was 1 slot). KV cache is
now asymmetric: K at q8_0, V at q5_0, both with Hadamard. The 3 slots
triple the KV, so expert blocks 27-34 are pinned back to CPU (freed
~8.7 GiB) leaving output-adjacent 35-40 on CUDA. Q8_0 model,
split-mode none, spec disabled (ik limits spec to 1 slot). AGENTS.md:
drop the scratch-port rule (test/bench processes cannot share the
single GPU with the running service anyway). Env, serve script, bats,
docs updated.
Switch to CRACK Q8_0 model (37.8 GB, mmproj f16). Servt as a single
160000-ctx slot (PARALLEL=1); speculative decoding stays disabled. KV
cache is q6_0 + Hadamard. iq5_k is not a valid KV-cache type in ik_llama
(cache whitelist excludes iq5_*); q8_KV reproduces the ggml.c:5427 view
assert even at PARALLEL=1/no-spec, so q6_0 is the verified working 6-bit
choice. Env, serve script, tests, and KB updated. Deployment was left
down after the isolation test; this commit redeploys it with the
verified Q8_0/160k/q6_0 config.
--jinja: ik_llama only renders a custom --chat-template-file as real Jinja
when --jinja is passed (inputs.use_jinja ? apply_jinja : non-jinja
fallback). Without it the Qwen-Sharp template's thinking/image/tool
formatting is silently dropped. Add USE_JINJA=1 -> --jinja.
Experts: the RTX 3090 had ~5.4 GiB headroom; pull expert blocks 30,32,33,34
back to CUDA (each block ~630 MiB) from CPU for lower latency. Update the
OVERRIDE_EXPERT regex, its test, and the KB with the headroom-check note.
ik_llama limits speculative decoding to a single server slot (-np 1) and
hard-exits at load if a spec stage is configured with --parallel > 1:
"Speculative decoding is currently limited to a single server slot
(-np 1)." This service keeps PARALLEL=3 (one slot per user), so set
SPEC_TYPE=none to disable speculation. It is a low-parameter MoE (3B
active of 35B), so throughput is fine without it. MTP stays disabled.
KB and tests updated.
The ik_llama fast q8_KV cache type crashes at model init on this
QWEN3MOE/MTP + multimodal arch at commit b166e269 with a ggml_view size
assert (ggml.c:5427) right after the output buffer is created, looping
the service. Empirically isolated by toggling cache type: q8_0 and f16
load and serve (smoke-tested correct output), q8_KV asserts. Switch to
q8_0 + Hadamard (K/V), which still gives the 8-bit cache + Hadamard
quality win. KB updated with the crash lesson.
--split-mode graph requires >= 2 devices in ik_llama.cpp and errors hard
at model load on a single RTX 3090 ("It is not possible to use split mode
'graph' with less than 2 devices"), looping the service. Set SPLIT_MODE
=none (the default), which works with the --override-tensor CPU expert
offload. KB updated with the two-device requirement so we do not assume
graph degrades gracefully on one GPU.
Switch frank-default-small on Dankserver from upstream llama.cpp (b10121)
to ikawrakow/ik_llama.cpp (b166e269, built with the staged pip cu13
toolkit). The model (Ornith-1.5-35B-A3B CRACK Q6_K) is unchanged and the
old binary is kept for rollback.
Runtime changes:
- KV cache quantized to q8_KV with --k-cache-hadamard/--v-cache-hadamard
- --split-mode graph (single-GPU no-op, retained per operator pref)
- migrate ngram speculation to canonical
--spec-type ngram-mod:n_min=48,n_max=64,ngram_size_n=24
(legacy --spec-ngram-mod-* are rejected in ik main)
- N_GPU_LAYERS 999 (ik -ngl rejects the literal "all"); experts stay
pinned to CPU via --override-tensor
Build: GGML_CUDA_FA_ALL_QUANTS=ON + GGML_IQK_FA_ALL_QUANTS=ON mandatory
for the quantized KV cache; recipe + reasoning baked into
docs/LLM/ikllama-ornith.md. Tests and both indices updated.
- frank-default-small (Dankserver): Ornith-1.5-35B-A3B-CRACK Q8_0, bundled
native draft-mtp (n_max 4), MoE expert FFN tensors pinned to CPU via an
explicit --override-tensor \.ffn_(up|down|gate|gate_up)_(ch|)exps=CPU
(replaces --n-cpu-moe), Qwen-Sharp chat template.
- frank-default-large (Dankclaw): kingjones Qwen3.8-27B-Uncensored-Q6_0
ROCmFP4 AGENT dense GGUF, standalone mtp-Qwen3.8-27B-Q4_0 draft head
(--model-draft, draft-mtp n_max 4), Qwen-Sharp chat template. Drop the
retired PromptForge .pfs sidecar lanes.
- Track scripts/chat-template.jinja (v22.4.0 Sharp) and apply via
--chat-template-file on both services.
- Docs: new kingjones-strix-mtp and dealign-ornith-crack runbook pages;
update index, deployment, and both system specs; models.json names.
Smoke-tested both models on the real hardware on scratch ports before
commit: model loads, MTP draft attaches, Sharp template applies, chat
completion returns.
The LLM fleet serve scripts, systemd units, env tunables, and bench
harnesses join the deploy scripts. The vllmScripts deploy.sh is
removed here; its functionality is folded into the unified
scripts/deploy.sh in a follow-up commit.
bench-spec-stack.sh gains the executable bit (required by the
scripts_syntax test seam).
Frank