docs+deploy: Dankserver now llama.cpp frank-default-small (HauhauCS Q6_K_P + DFlash)

Retire vllm-awq-qwen36 (AWQ) from target maps, README, deployment, KB index,
Dankserver specs, DFlash and FP8 docs. New dankserver runtime: upstream
llama.cpp, port 7770, alias frank-default-small, 3x262144 ctx, DFlash Q8_0
draft, MoE experts offloaded to CPU, attention+KV on GPU (K q8_0, V f16).
deploy.sh now maps dankserver to frank-default-small and symlinks its unit.
models.json: dankserver baseUrl -> :7770, contextWindow -> 262144.

docs/DFlash DSpark Speculative Decoding.md

@@ -115,7 +115,9 @@ The DFlash draft is not a standalone model. It must pair with its target model.
 
 ## Deployment on Dankserver (NVIDIA RTX 3090)
 
-Dankserver runs vLLM in a Podman container with the RTX 3090. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
+The active Dankserver deployment runs **llama.cpp + DFlash** (`frank-default-small`), not vLLM. It serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` on port 7770 with the `Alittlehammmer/Qwen3.6-35B-A3B-DFlash` Q8_0 draft, 3 slots at 262144 context, and MoE experts on CPU. See [[deployment]] and `scripts/serve-llama-frank-small.sh`.
+
+The vLLM material below documents the archived AWQ/DFlash path on the same hardware. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
 
 - FP8 KV cache is valid on CUDA 12.
 - FP8 weights compute through a fallback path; no native FP8 tensor cores.

docs/Dankserver System Specs.md

@@ -51,15 +51,14 @@ Dankserver is an NVIDIA GPU server for vLLM inference. The host runs Ubuntu 24.0
 
 ### Active Services
 
-A vLLM engine core (Podman container `vllm-qwen36-dflash`) occupies approximately 22 GB of the RTX 3090 VRAM (`nvidia-smi` reports `VLLM::EngineCore` at 22164 MiB).
+The active `frank-default-small` service (llama.cpp, `b10108+`) serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model on port 7770 with a DFlash draft. It runs 3 parallel slots at 262144 context each. All 40 layers' MoE expert weights sit on CPU (`--n-cpu-moe 40`) while non-expert tensors and the full-attention KV cache stay on GPU (`--gpu-layers all`, KV K=`q8_0`, V=`f16`). At load it uses about 22 GB of the 24 GB VRAM and roughly 4 GB of resident RAM (experts are lazy-mapped).
 
-## vLLM Relevance
+## Hardware Relevance
 
-- 24 GB VRAM limits model size. Use FP8 quantization for large models.
-- 62 GB system RAM provides ample swap space for KV cache offloading.
-- 127 GB swap supports large `--swap-space` values for CPU KV cache paging.
-- Root disk has approximately 140 GB free. Store models on /mnt/entropy.
-- CUDA 12.0 and Python 3.12.3 are compatible with vLLM.
+- 24 GB VRAM limits the GPU-resident footprint. With 3 x 262144 context, the full-attention KV cache plus non-expert weights consume about 22 GB, leaving about 2 GB of headroom. MoE experts must stay on CPU.
+- 62 GB system RAM holds the ~29 GB of MoE expert weights with room to spare (~57 GB available after load).
+- 127 GB swap is available for CPU KV cache paging if GPU KV offload is disabled.
+- Root disk has approximately 140 GB free. Store the GGUF models under `/home/dank/models` (about 32 GB total).
 
 ## See Also
 

docs/Running Qwen3.6 35B A3B FP8 on Dankserver.md

@@ -112,7 +112,7 @@ export HF_HOME=/mnt/entropy/huggingface
 
 ### Active Service Conflict
 
-A vLLM engine core (Podman container `vllm-qwen36-dflash`) occupies roughly 22 GB of the 24 GB VRAM. Stop it before starting a competing service:
+The active `frank-default-small` service (llama.cpp, port 7770) uses about 22 GB of the 24 GB VRAM. The retired `vllm-awq-qwen36` (Podman, ~22 GB) and `vllm-qwen36` services are not active. Stop any competing service before starting another:
 
 ```
 podman stop vllm-qwen36-dflash

docs/deployment.md

@@ -9,7 +9,7 @@ Two servers run LLM inference services. Each server pulls its scripts from this
 | Target | Host | Runtime | Service | Port |
 |--------|------|---------|---------|------|
 | Dankclaw | dank@dankclaw | vLLM-ROCm (CIRU INT4) | frank-default-large | 7770 |
-| Dankserver | dank@dankserver | vLLM (Docker) | vllm-awq-qwen36 | 34401 |
+| Dankserver | dank@dankserver | llama.cpp (Q6_K_P + DFlash, MoE offload) | frank-default-small | 7770 |
 
 ## Workflow
 
@@ -90,9 +90,10 @@ Run these commands on Dankserver:
 ```
 cd /home/dank/vLLM
 git pull origin main
+sudo ln -sf /home/dank/vLLM/scripts/frank-default-small.service /etc/systemd/system/frank-default-small.service
 sudo systemctl daemon-reload
-sudo systemctl restart vllm-qwen36
-curl -sf http://127.0.0.1:8000/health
+sudo systemctl restart frank-default-small
+curl -sf http://127.0.0.1:7770/health
 ```
 
 ## Service Units
@@ -101,11 +102,11 @@ curl -sf http://127.0.0.1:8000/health
 
 The active service on Dankclaw runs a vLLM-ROCm runtime for the Ling-3.0-flash CIRU INT4 (W4A16) model. It serves under the alias `frank-default-large` on port 7770 with native MTP speculative decoding. The unit file is `scripts/frank-default-large.service` in the repository. The machine symlinks it to `/etc/systemd/system/frank-default-large.service`. The service launches through `scripts/serve-ling3-vllm.sh`, which sources the CIRU runtime and passes the production settings (host `0.0.0.0`, port 7770, gpu memory utilization 0.95).
 
-### Dankserver — vllm-awq-qwen36
+### Dankserver — frank-default-small
 
-The active vLLM service on Dankserver runs in a Podman/Docker container and serves the Qwen3.6-35B-A3B AWQ 4-bit model on port 34401. The unit file is a physical file on the machine, not a symlink into the repository.
+The active service on Dankserver runs llama.cpp (upstream, `b10108+`) and serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` model under the alias `frank-default-small` on port 7770. It uses a DFlash draft (`Alittlehammmer/Qwen3.6-35B-A3B-DFlash:Q8_0`) with `--spec-draft-n-max 7`, 3 parallel slots at 262144 context each, and mmproj vision. All 40 layers' MoE experts stay on CPU (`--n-cpu-moe 40`) with non-expert tensors on GPU (`--gpu-layers all`); the full-attention KV cache stays on GPU with K quantized to `q8_0` and V at `f16`. The unit file is `scripts/frank-default-small.service`, symlinked to `/etc/systemd/system/`. It launches through `scripts/serve-llama-frank-small.sh`, which reads tunables from `scripts/frank-default-small.env`.
 
-> Note: the failed `vllm-qwen36.service` (FP8) and the retired `llama-server.service` (ROCmFPX llama.cpp) are not active. Verify the intended service before a restart. See the repository `git log` for the latest decision.
+> Note: this service replaced the retired `vllm-awq-qwen36` (AWQ 4-bit, port 34401) and the earlier `vllm-qwen36` (FP8) / `llama-server` (ROCmFPX) units. The repo tracks `main` on Dankserver at `/home/dank/vLLM`.
 
 ## Validation
 

docs/index.md

@@ -8,10 +8,10 @@ Documentation for running vLLM inference on two local machines.
 graph LR
     subgraph Dankserver[NVIDIA Dankserver - Ubuntu 24.04]
         A[RTX 3090 24GB + 62GB RAM]
-        B[vLLM in Podman
-vllm-awq-qwen36]
-        C[Qwen3.6-35B-A3B AWQ 4bit
-port 34401]
+        B[llama.cpp DFlash
+frank-default-small]
+        C[Qwen3.6-35B-A3B uncensored Q6_K_P
+experts on CPU, port 7770]
         A --> B --> C
     end
 
@@ -24,7 +24,7 @@ port 7770]
         D --> E --> F
     end
 
-    G[LLM Clients / API] -->|HTTP :34401| B
+    G[LLM Clients / API] -->|HTTP :7770| B
     G -->|HTTP :7770| E
 ```