# 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.

## New Topology

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

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

## Models

### frank-default-large — 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-default-small — Avesed/Qwen3.6-35B-A3B-INT8-W8A8

- 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: compressed-tensors INT8 W8A8 (symmetric per-channel weights,
  dynamic per-token activations). Expert FFNs quantized; attention, router,
  DeltaNet, lm_head kept bf16. Weights ~38 GB.
- Served with vLLM ROCm, ngram speculative decoding.

## Context Math (frank-default-large, RTX 3090)

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

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-default-large.env` to 122880 for ~1.8 GiB headroom.

## Install Runbook

### Dankserver (frank-default-large)

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-default-large.service /etc/systemd/system/
   sudo systemctl daemon-reload && sudo systemctl enable frank-default-large
   sudo systemctl restart frank-default-large
   ```
5. Verify: `curl -sf http://127.0.0.1:7770/health` and a chat completion.
6. Confirm VRAM headroom with `nvidia-smi`.

### Dankclaw (frank-default-small)

1. Stop the old services: `sudo systemctl stop frank-default-large
   frank-default-small` (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-default-small.service /etc/systemd/system/
   sudo systemctl daemon-reload && sudo systemctl enable frank-default-small
   sudo systemctl restart frank-default-small
   ```
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.
- HF token: `~/.cache/huggingface/token` on both machines (mode 600).
- OpenWebUI (sibling repo): retarget `OPENAI_API_BASE_URL` to the
  Dankserver endpoint.