serverAppConfigs/serverAppConfigs-docs/LLM/frank-redo-2026-08-17.md

---
type: reference
description: "This page records the full redo of the inference fleet on 2026-08-17."
tags:
  - llm
  - frank
timestamp: 2026-09-03
---

# Frank Program Redo — 2026-08-17

This page records the full redo of the inference fleet on 2026-08-17.
The pipeline-parallel split and the FastFlowLM NPU endpoint were retired.
The old scripts moved to `old/scripts/` in this repository.

> **HISTORY — the topology below is two fleet generations dead.** Today the
> fleet is three endpoints, each owned by its page: [[frank-archon]]
> (`192.168.0.235:7770`), [[frank-dragoon]] (`192.168.0.143:7770`) and
> [[frank-zealot]] (`192.168.0.143:7771`). No vLLM and no ROCm endpoint serves
> anything; there is no pipeline parallelism and no NPU endpoint.

## New Topology

| Service | Host | Runtime | Model | Port |
|---------|------|---------|-------|------|
| frank-dragoon | Dankserver | llama.cpp (patched FastMTP) | HauhauCS Qwen3.8-27B-Uncensored-Aggressive IQ4_XS | 7770 |
| frank-zealot | Dankclaw | vLLM ROCm | nameistoken Qwen3.6-35B-A3B Quark W8A8 INT8 | 52625 |

Names as the 2026-08-17 commit wrote them: `frank-default-large` on Dankserver
(`git show d9af7f1:scripts/frank-default-large.env:20,22` → `ALIAS=frank-default-large`,
`LISTEN_PORT=7770`) and `frank-default-small` on Dankclaw
(`git show d9af7f1:scripts/frank-default-small.env:16,18` → `ALIAS=frank-default-small`,
`LISTEN_PORT=52625`); the Protoss ids date from 2026-10-06 → 10-10.

The RTX 3090 runs the whole 27B model standalone. No RPC worker, no
tensor split, no NPU.

## Models

### frank-dragoon — HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ4_XS

- Base: Qwen3.8-27B (dense 27B, 64 layers: 48 GatedDeltaNet + 16
  gated-attention, hidden 5120, 24 Q heads / 4 KV heads, head dim 256,
  native 262144 ctx).
- Quant: IQ4_XS (4.60 BPW, 15.71 GB). Every GGUF in the repo preserves the
  native embedded MTP/NextN head.
- FastMTP draft sidecar: `...FastMTP-32K.gguf` (903 MB). Requires the
  patched llama.cpp build (commit `4df29be4f4c3673f428170fda944a5b19f743bb8`
  + `HauhauCS-FastMTP-llama.cpp.patch`). Without the patch, the draft load
  fails with `expected 5120, 248320, got 5120, 32768`.
- Spec chain: FastMTP (`--spec-type draft-mtp`, `--spec-draft-n-max 2`,
  `--spec-draft-ngl all`, `--spec-draft-p-min 0`) then ngram-mod fallback
  (48/64/24).

### frank-zealot — nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8

- Base: Qwen3.6-35B-A3B (hybrid MoE, 256 experts top-8, ~3B active, 40
  layers: 10 blocks of 3 DeltaNet + 1 Attention, native 262144 ctx).
- Quant: AMD Quark W8A8 INT8 (per-channel static weights, per-token
  dynamic activations). MoE experts stored as per-expert nn.Linear
  triplets that match vLLM's FusedMoE mapping exactly. Weights ~35 GB;
  the vision tower, router, lm_head stay BF16.
- Served with vLLM ROCm, ngram speculative decoding, and tool calling.
- Swap history: the initial deployment used
  `Avesed/Qwen3.6-35B-A3B-INT8-W8A8` (compressed-tensors INT8). Its tool
  calls were malformed (the model emitted `<function=get_weather(city=...)>`
  instead of the Qwen3 XML structure). The Quark quant follows the format
  cleanly; swapped in the evening of 2026-08-17.

## Context Math (frank-dragoon, RTX 3090)

KV per token: 16 attention layers x 4 KV heads x 256 dim.
K q8_0 = 1088 B per layer (4 KV heads × 256 dim × 1.0625 B), V f16 = 2048 B per
layer, ×16 layers = 50176 B/token total.

Budget (MiB): 24576 total - 14982 weights - 861 draft - 1024 fixed
(CUDA context, compute graph, scratch, draft KV) - headroom.

| Context | KV (MiB) | Free after load |
|---------|----------|-----------------|
| 114688 (112K) | 5488 | 2221 (2.17 GiB) |
| 122880 (120K) | 5880 | 1829 (1.79 GiB) |
| 131072 (128K) | 6272 | 1437 (1.40 GiB) |
| 147456 (144K) | 7056 | 653 (0.64 GiB) |

Production choice: **131072 (128K)** — the maximum that keeps about
1.4 GiB headroom for checkpoints. Lower `CTX_SIZE` in
`scripts/frank-dragoon.env` to 122880 for ~1.8 GiB headroom.

## Install Runbook

### Dankserver (frank-dragoon)

1. Stop the old worker: `sudo systemctl stop frank-pp-worker`.
2. Download the model and draft to `/home/dank/models/hauhaucs-qwen38/`.
3. Build the patched llama.cpp:
   ```
   git clone https://github.com/ggerganov/llama.cpp /home/dank/llama.cpp-fastmtp
   cd /home/dank/llama.cpp-fastmtp
   git checkout 4df29be4f4c3673f428170fda944a5b19f743bb8
   curl -L -o HauhauCS-FastMTP-llama.cpp.patch <HF patch URL>
   git apply HauhauCS-FastMTP-llama.cpp.patch
   cmake -S . -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
     -DGGML_CUDA_FA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON
   cmake --build build --config Release -j$(nproc)
   ```
   `GGML_CUDA_FA_ALL_QUANTS=ON` is mandatory. Without it, prefill collapses
   ~12x (the q8_0 KV + flash attention falls back to a slow path;
   measured 95 t/s vs 1300+ t/s at 8K on the 3090).
4. Symlink the unit, reload, enable, restart:
   ```
   sudo ln -sf /home/dank/vLLM/scripts/frank-dragoon.service /etc/systemd/system/
   sudo systemctl daemon-reload && sudo systemctl enable frank-dragoon
   sudo systemctl restart frank-dragoon
   ```
5. Verify: `curl -sf http://127.0.0.1:7770/health` and a chat completion.
6. Confirm VRAM headroom with `nvidia-smi`.

### Dankclaw (frank-zealot)

1. Stop the old services: `sudo systemctl stop frank-dragoon
   frank-zealot` (retired PP head and NPU endpoint).
2. Install uv and create the venv:
   ```
   curl -LsSf https://astral.sh/uv/install.sh | sh
   uv venv --python /usr/bin/python3.12 /home/dank/vllm-venv
   uv pip install --python /home/dank/vllm-venv vllm --torch-backend=auto
   ```
   The resolver pulls the AMD ROCm wheel set (torch +
   amd-torch-device-gfx1151 + the vllm rocm variant) from the AMD index.
3. Cache the model (default HF cache):
   ```
   python3 -c "from huggingface_hub import snapshot_download; snapshot_download('Avesed/Qwen3.6-35B-A3B-INT8-W8A8')"
   ```
4. Install the ROCm wheel set and the flash-attention triton path:
   ```
   uv pip install torch --torch-backend=auto
   uv pip install "vllm==0.27.1+rocm723" --index-url https://wheels.vllm.ai/rocm/ \
     --extra-index-url https://pypi.org/simple --index-strategy unsafe-best-match --no-build-isolation
   cp -r <ciru-venv>/site-packages/flash_attn* <vllm-venv>/site-packages/
   # add a flash_attn_2_cuda stub module (ROCm build has no compiled .so)
   ```
   Set `FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE` (env file). Without it the
   vision encoder falls back to torch SDPA and engine init OOMs.
5. Symlink the unit, reload, enable, restart:
   ```
   sudo ln -sf /home/dank/vllmScripts/scripts/frank-zealot.service /etc/systemd/system/
   sudo systemctl daemon-reload && sudo systemctl enable frank-zealot
   sudo systemctl restart frank-zealot
   ```
6. Verify: `curl -sf http://127.0.0.1:52625/health` and a chat completion.

## Fallback: vLLM source build for gfx1151

If the prebuilt ROCm vLLM wheel cannot run on gfx1151, build from source.
The retired CIRU package documents the proven recipe:
`/home/dank/ling3-ciru/Ling-3.0-Flash-CIRU-int4-Strix-native/scripts/build-vllm-gfx1151.sh`
(branch `ciru/ling3-gfx1151-rocm715`, index `https://rocm.nightlies.amd.com/whl-multi-arch/`).
The upstream starting point needed a five-line ROCm sampler fix to launch
on gfx1151; newer vLLM releases may already include it.

## Configuration

- `models.json` (pi): two providers — `llamacpp (dankserver)` at
  http://192.168.0.235:7770/v1 and `vllm (dankclaw)` at
  http://192.168.0.143:52625/v1. (The harness config today has three providers
  and no vLLM: `~/.omp/agent/models.yml:14,94,173`.)
- HF token: `~/.cache/huggingface/token` on both machines (mode 600).
- OpenWebUI (sibling repo): retarget `OPENAI_API_BASE_URL` to the
  Dankserver endpoint.

## Evening Session (2026-08-17): tool calling, model swap, batch 8192

Three changes landed after the initial deployment:

### 1. Tool calling on frank-zealot (HTTP 400 fix)

Clients (pi) send `tool_choice: "auto"`. vLLM rejects it with HTTP 400
unless the server enables tool calling. Fixes applied:

- `--enable-auto-tool-choice --tool-call-parser qwen3_xml` (the Avesed /
  Quark chat template renders the Qwen3 XML tool format, and qwen3_xml
  is the matching parser).
- Custom chat template `/home/dank/models/qwen36-chat_template-tools.jinja`:
  derived from the repo's `chat_template.jinja` (the tokenizer_config.json
  ships no template, so vLLM would use its built-in one, which does not
  render the XML tool instructions). The custom template adds:
  - a concrete WORKED EXAMPLE built from the actual tool schema
    (name + parameter keys) — models follow concrete examples much
    better than the generic placeholder;
  - tool requests default to non-thinking (`enable_thinking` unset + tools
    present -> empty think block). Qwen3 tool calls only work reliably
    with thinking off; normal requests keep thinking.

Known pitfall: the earlier Avesed model still emitted malformed XML even
with the strong template (`<function=get_weather(city=...)>`); that was
model behavior, which motivated the swap.

### 2. Model swap: Avesed INT8 W8A8 -> Quark W8A8 INT8

`frank-zealot` now serves
`nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8` (AMD Quark 0.11.1, ~35 GB,
vLLM-native expert layout). The Avesed compressed-tensors quant's
Qwen3-XML tool fidelity was poor. Verified with the Quark model: plain
tool requests (no chat_template_kwargs) return clean
`tool_calls: [{"name": "get_weather", "arguments": {"city": "Paris"}}]`;
non-tool requests keep reasoning. The Avesed checkpoint stays in the HF
cache for rollback.

### 3. Batch 8192

`MAX_NUM_BATCHED_TOKENS` lowered from 16384 to 8192 (user request).
vLLM has no separate ubatch knob; the scheduler issues one batch per
step up to this token cap.

### 4. Prefix caching enabled

Added `--enable-prefix-caching`. The model is hybrid (GatedDeltaNet +
full attention); vLLM keeps prefix caching **opt-in** for hybrid models
(`default_prefix_caching = is_prefix_caching_supported and not
is_hybrid`), so it was off by default. Enabling it reuses the KV of
repeated prompt prefixes. Measured on a 2023-token prefix (2026-08-17
evening): 4.6s cold -> 1.1s cached (~4x TTFT), output unchanged. This is
the main TTFT lever for pi, which re-sends the same system prompt + tool
schema every turn. Toggled via `ENABLE_PREFIX_CACHING` in the env file.